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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:Simple Summary Neoadjuvant chemotherapy (NAC) followed with surgery is the standard strategy in the treatment of locally advanced breast cancer, but the individual efficacy varies. Early and accurate prediction of complete responders determines the NAC regimens and prognosis. Breast MRI has been recommended to monitor NAC response before, during, and after treatment. Radiomics has been heralded as a breakthrough in medicine and regarded to have changed the landscape of biomedical research in oncology. Delta-radiomics characterizing the change in feature values by applying radiomics to multiple time points, is a promising strategy for predicting response after NAC. In our study, the delta-radiomics model built with the change of radiomic features before and after one cycle NAC could effectively predict pathological complete response (pCR) in breast cancer. The model provides strong support for clinical decision-making at the earliest stage and helps patients benefit the most from NAC. Abstract Objective: To investigate the value of delta-radiomics after the first cycle of neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced (DCE) MRI for early prediction of pathological complete response (pCR) in patients with breast cancer. Methods: From September 2018 to May 2021, a total of 140 consecutive patients (training, n = 98: validation, n = 42), newly diagnosed with breast cancer who received NAC before surgery, were prospectively enrolled. All patients underwent DCE-MRI at pre-NAC (pre-) and after the first cycle (1st-) of NAC. Radiomic features were extracted from the postcontrast early, peak, and delay phases. Delta-radiomics features were computed in each contrast phases. Least absolute shrinkage and selection operator (LASSO) and a logistic regression model were used to select features and build models. The model performance was assessed by receiver operating characteristic (ROC) analysis and compared by DeLong test. Results: The delta-radiomics model based on the early phases of DCE-MRI showed a highest AUC (0.917/0.842 for training/validation cohort) compared with that using the peak and delay phases images. The delta-radiomics model outperformed the pre-radiomics model (AUC = 0.759/0.617, p = 0.011/0.047 for training/validation cohort) in early phase. Based on the optimal model, longitudinal fusion radiomic models achieved an AUC of 0.871/0.869 in training/validation cohort. Clinical-radiomics model generated good calibration and discrimination capacity with AUC 0.934 (95%CI: 0.882, 0.986)/0.864 (95%CI: 0.746, 0.982) for training and validation cohort. Delta-radiomics based on early contrast phases of DCE-MRI combined clinicopathology information could predict pCR after one cycle of NAC in patients with breast cancer.

Project description:BackgroundThough contrast-enhanced ultrasound (CEUS) perfusion parameters have been approved to be potential indicators for response to chemotherapy in solid tumors, their ability in assessment of colorectal liver metastasis (CRLM) to chemotherapy with bevacizumab (Bev) has rarely been investigated.MethodsFrom March 2021 to May 2022, 115 consecutive CRLM patients with CEUS pre- and post-2 months' chemotherapy with Bev were prospectively enrolled. One target lesion per patient underwent CEUS quantitative analysis with SonoLiver software. Rise time, time-to-peak, mean transit time, maximal intensity (IMAX), and area under the time-intensity curve (AUC) were assessed with region of interest (ROI) selected on whole lesion, lesion periphery, and internal lesion, respectively. The reduction and ratio of post- to pre-treatment in parameters were investigated in development cohort (n=89) and validated in internal validation cohort (n=26) according to the chronological order.ResultsWith modified Response Evaluation Criteria in Solid Tumor as reference, 48, 14 responders and 41, 12 non-responders were included in development and validation cohort, respectively. Significantly smaller values of IMAX and AUC on ROIwhole, ROIperipheral, and ROIinternal, were observed post-treatment in development cohort (all P<0.05). In predicting treatment response, the influence of ROI selection was observed when using ∆IMAX and ∆AUC, while no influence was observed using ratios. Areas under the receiver operating characteristic curve (AUROCs) for ∆IMAX and ∆AUC on ROIperipheral were 0.939 (0.867-0.979), 0.951 (0.883-0.985), and 0.917 (0.740-0.988), 0.923 (0.748-0.990) in development and validation cohort, respectively. For ratios of IMAX and AUC, AUROCs were 0.976 (0.919-0.997), 0.938 (0.865-0.978), and 0.899 (0.717-0.982), 0.982 (0.836-1.000) in development and validation cohort, respectively.ConclusionsIMAX and AUC showed significant reductions in responders, and different analyses ROIs influence the performance of ∆IMAX and ∆AUC in response assessment. Parameters derived from ROI peripheral exhibited the most promising results in predicting treatment response.

Project description:ObjectiveTo develop and validate a radiomics nomogram based on pre-treatment, early treatment ultrasound (US) radiomics features combined with clinical characteristics for early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer.MethodA total of 217 patients with histological results of breast cancer receiving four to eight cycles of NAC before surgery from January 2018 to December 2020 were enrolled. Patients from the study population were randomly separated into a training set (n = 152) and a validation set (n = 65) at a ratio of 7:3. A total of 788 radiomics features were extracted from each region of interest in the US image at pre-treatment baseline (radiomic signature, RS1), early treatment (after completion of two cycles of NAC, RS2) and delta radiomics (calculated between the pre-treatment and post-treatment features, Delta RS). The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection. The predictive nomogram was built based on the radiomics signature combined with clinicopathological risk factors. Discrimination, calibration, and prediction performance were further evaluated in the validation set.ResultsOf the 217 breast masses, 127 (58.5%) were responsive to NAC and 90 (41.5%) were non-responsive. Following feature selection, nine features in RS1, 11 features in RS2, and eight features in Delta RS remained. With multivariate analysis, the RS1, RS2, Delta RS, and Ki-67 expression were independently associated with breast NAC response. However, the performance of the Delta RS (AUC Delta RS = 0.743) was not higher than RS1 (AUC RS1 = 0.722, PDelta vs RS1 = 0.086) and RS2 (AUC RS2 = 0.811, PDelta vs RS2 = 0.173) with the Delong test. The nomogram incorporating RS1, RS2, and Ki-67 expression showed better predictive ability for NAC response with an area under the curve (AUC) of 0.866 in validation cohorts than either the single RS1 (AUC 0.725) or RS2 (AUC 0.793) or Ki-67 (AUC 0.643).ConclusionThe nomogram incorporating pre-treatment and early-treatment US radiomics features and Ki-67 expression showed good performance in terms of NAC response in breast cancer, thereby providing valuable information for individual treatment and timely adjustment of chemotherapy regimens.

Project description:BackgroundNeoadjuvant chemotherapy has gradually become an important means of breast cancer treatment; however, tumor regression following chemotherapy remains a concern. This study was conducted to investigate the effect of ultrasound-assisted carbon nanoparticle labeling in neoadjuvant chemotherapy for breast-conserving surgery in breast cancer.MethodsThis was a prospective clinical trial study (clinical registration number: ChiCTR-OOC-15006844). Sixty-eight breast cancer patients confirmed by biopsy between July 2015 and January 2017 were randomly selected from the clinical data. Of these, 32 patients were screened for neoadjuvant chemotherapy, forming a consecutive, random series. An ultrasound-guided carbon nanotube was used to mark the original tumor, and sentinel lymph node biopsies were performed. After 4-6 cycles of standard neoadjuvant chemotherapy, 26 patients were selected for breast-conserving surgery. The feasibility and validity of carbon nanoparticle labeling were analyzed through the negative rate of incision margin, the volume of resected tumors, the detection rate of black-stained sentinel lymph nodes, the recurrence rate of ipsilateral breast, and postoperative survival.ResultsIn all, 32 patients underwent sentinel lymph node biopsy, 29 cases were detected (90.6%), the false-negative rate was 3.8% (1/26), and 0-4 sentinel lymph nodes (mean 1.8±1.1) were detected. A total of 26 patients underwent breast-conserving surgery, 5 underwent secondary excision, and 1 underwent subcutaneous adenectomy due to a positive margin. The minimum margin between the resected site and the infiltrated part was 1.0-2.1 cm (1.3±0.3 cm). The diameter of resected tumors ranged from 2.2 to 4.5 cm (3.1±0.6 cm). No recurrence or distant metastasis of ipsilateral breast tumors was observed during follow-up (the median follow-up time was 9 months).ConclusionsUltrasound-assisted carbon nanoparticle labeling is effective for sentinel lymph node tracing before neoadjuvant chemotherapy and has a high detection rate for metastatic lymph nodes. During breast-conserving surgery, it can determine the extent of tumor resection to achieve precision surgical treatment.

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.

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

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