Low-frequency quantitative ultrasound imaging of cell death in vivo

Ultrasound Spectral Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastatic in Breast Cancer

OBJECTIVES

To explore the clinical value of the nomogram based on ultrasound spectral combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer.

METHODS

We prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively.

RESULTS

Univariate analysis showed that lymph node metastasis was related with tumor size, Ki-67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki-67, axillary ultrasound, quantitative parameter (the mean of the mid-band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki-67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C-index of 0.83 (95% confidence interval 0.73-0.93).

CONCLUSION

Axillary ultrasound combined with Ki-67 and ultrasound spectral parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.

In Vivo Validation of an In Situ Calibration Bead as a Reference for Backscatter Coefficient Calculation

OBJECTIVE

The study described here was aimed at assessing the capability of quantitative ultrasound (QUS) based on the backscatter coefficient (BSC) for classifying disease states, such as breast cancer response to neoadjuvant chemotherapy and quantification of fatty liver disease. We evaluated the effectiveness of an in situ titanium (Ti) bead as a reference target in calibrating the system and mitigating attenuation and transmission loss effects on BSC estimation.

METHODS

Traditional BSC estimation methods require external references for calibration, which do not account for ultrasound attenuation or transmission losses through tissues. To address this issue, we used an in situ Ti bead as a reference target, because it can be used to calibrate the system and mitigate the attenuation and transmission loss effects on estimation of the BSC. The capabilities of the in situ calibration approach were assessed by quantifying consistency of BSC estimates from rabbit mammary tumors (N = 21). Specifically, mammary tumors were grown in rabbits and when a tumor reached ≥1 cm in size, a 2 mm Ti bead was implanted in the tumor as a radiological marker and a calibration source for ultrasound. Three days later, the tumors were scanned with an L-14/5 38 array transducer connected to a SonixOne scanner with and without a slab of pork belly placed on top of the tumors. The pork belly acted as an additional source of attenuation and transmission loss. QUS parameters, specifically effective scatterer diameter (ESD) and effective acoustic concentration (EAC), were calculated using calibration spectra from both an external reference phantom and the Ti bead.

RESULTS

For ESD estimation, the 95% confidence interval between measurements with and without the pork belly layer was 6.0, 27.4 using the in situ bead and 114, 135.1 with the external reference phantom. For EAC estimation, the 95% confidence intervals were -8.1, 0.5 for the bead and -41.5, -32.2 for the phantom. These results indicate that the in situ bead method has reduced bias in QUS estimates because of intervening tissue losses.

CONCLUSION

The use of an in situ Ti bead as a radiological marker not only serves its traditional role but also effectively acts as a calibration target for QUS methods. This approach accounts for attenuation and transmission losses in tissue, resulting in more accurate QUS estimates and offering a promising method for enhanced disease state classification in clinical settings.

Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning

Objective

Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response to NAC for patients with Locally Advanced Breast Cancer (LABC) before treatment initiation could be beneficial to optimize therapy, ensuring the administration of effective treatments. The objective of the work here was to develop a predictive model to predict tumor response to NAC for LABC using deep learning networks and computed tomography (CT).

Materials and methods

Several deep learning approaches were investigated including ViT transformer and VGG16, VGG19, ResNet-50, Res-Net-101, Res-Net-152, InceptionV3 and Xception transfer learning networks. These deep learning networks were applied on CT images to assess the response to NAC. Performance was evaluated based on balanced_accuracy, accuracy, sensitivity and specificity classification metrics. A ViT transformer was applied to utilize the attention mechanism in order to increase the weight of important part image which leads to better discrimination between classes.

Results

Amongst the 117 LABC patients studied, 82 (70%) had clinical-pathological response and 35 (30%) had no response to NAC. The ViT transformer obtained the best performance range (accuracy = 71 ± 3% to accuracy = 77 ± 4%, specificity = 86 ± 6% to specificity = 76 ± 3%, sensitivity = 56 ± 4% to sensitivity = 52 ± 4%, and balanced_accuracy=69 ± 3% to balanced_accuracy=69 ± 3%) depending on the split ratio of train-data and test-data. Xception network obtained the second best results (accuracy = 72 ± 4% to accuracy = 65 ± 4, specificity = 81 ± 6% to specificity = 73 ± 3%, sensitivity = 55 ± 4% to sensitivity = 52 ± 5%, and balanced_accuracy = 66 ± 5% to balanced_accuracy = 60 ± 4%). The worst results were obtained using VGG-16 transfer learning network.

Conclusion

Deep learning networks in conjunction with CT imaging are able to predict the tumor response to NAC for patients with LABC prior to start. A ViT transformer could obtain the best performance, which demonstrated the importance of attention mechanism.

Enhanced full-inversion-based ultrasound elastography for evaluating tumor response to neoadjuvant chemotherapy in patients with locally advanced breast cancer

PURPOSE

An enhanced ultrasound elastography technique is proposed for early assessment of locally advanced breast cancer (LABC) response to neoadjuvant chemotherapy (NAC).

METHODS

The proposed elastography technique inputs ultrasound radiofrequency data obtained through tissue quasi-static stimulation and adapts a strain refinement algorithm formulated based on fundamental principles of continuum mechanics, coupled with an iterative inverse finite element method to reconstruct the breast Young's modulus (E) images. The technique was explored for therapy response assessment using data acquired from 25 LABC patients before and at weeks 1, 2, and 4 after the NAC initiation (100 scans). The E ratio of tumor to the surrounding tissue was calculated at different scans and compared to the baseline for each patient. Patients' response to NAC was determined many months later using standard clinical and histopathological criteria.

RESULTS

Reconstructed E ratio changes obtained as early as one week after the NAC onset demonstrate very good separation between the two cohorts of responders and non-responders to NAC. Statistically significant differences were observed in the E ratio changes between the two patient cohorts at weeks 1 to 4 after treatment (p-value < 0.001; statistical power greater than 97%). A significant difference in axial strain ratio changes was observed only at week 4 (p-value = 0.01; statistical power = 76%). No significant difference was observed in tumor size changes at weeks 1, 2 or 4.

CONCLUSION

The proposed elastography technique demonstrates a high potential for chemotherapy response monitoring in LABC patients and superior performance compared to strain imaging.

Quantitative Ultrasound for Evaluation of Tumour Response to Ultrasound-Microbubbles and Hyperthermia

Objectives: Prior study has demonstrated the implementation of quantitative ultrasound (QUS) for determining the therapy response in breast tumour patients. Several QUS parameters quantified from the tumour region showed a significant correlation with the patient's clinical and pathological response. In this study, we aim to identify if there exists such a link between QUS parameters and changes in tumour morphology due to combined ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) using the breast xenograft model (MDA-MB-231). Method: Tumours grown in the hind leg of severe combined immuno-deficient mice were treated with permutations of USMB and HT. Ultrasound radiofrequency data were collected using a 25 MHz array transducer, from breast tumour-bearing mice prior and post-24-hour treatment. Result: Our result demonstrated an increase in the QUS parameters the mid-band fit and spectral 0-MHz intercept with an increase in HT duration combined with USMB which was found to be reflective of tissue structural changes and cell death detected using haematoxylin and eosin and terminal deoxynucleotidyl transferase dUTP nick end labelling stain. A significant decrease in QUS spectral parameters was observed at an HT duration of 60 minutes, which is possibly due to loss of nuclei by the majority of cells as confirmed using histology analysis. Morphological alterations within the tumour might have contributed to the decrease in backscatter parameters. Conclusion: The work here uses the QUS technique to assess the efficacy of cancer therapy and demonstrates that the changes in ultrasound backscatters mirrored changes in tissue morphology.

Implementation of Non-Invasive Quantitative Ultrasound in Clinical Cancer Imaging

Quantitative ultrasound (QUS) is a non-invasive novel technique that allows treatment response monitoring. Studies have shown that QUS backscatter variables strongly correlate with changes observed microscopically. Increases in cell death result in significant alterations in ultrasound backscatter parameters. In particular, the parameters related to scatterer size and scatterer concentration tend to increase in relation to cell death. The use of QUS in monitoring tumor response has been discussed in several preclinical and clinical studies. Most of the preclinical studies have utilized QUS for evaluating cell death response by differentiating between viable cells and dead cells. In addition, clinical studies have incorporated QUS mostly for tissue characterization, including classifying benign versus malignant breast lesions, as well as responder versus non-responder patients. In this review, we highlight some of the important findings of previous preclinical and clinical studies and expand the applicability and therapeutic benefits of QUS in clinical settings. We summarized some recent clinical research advances in ultrasound-based radiomics analysis for monitoring and predicting treatment response and characterizing benign and malignant breast lesions. We also discuss current challenges, limitations, and future prospects of QUS-radiomics.

Predicting the outcome of radiotherapy in brain metastasis by integrating the clinical and MRI-based deep learning features

BACKGROUND

A considerable proportion of metastatic brain tumors progress locally despite stereotactic radiation treatment, and it can take months before such local progression is evident on follow-up imaging. Prediction of radiotherapy outcome in terms of tumor local failure is crucial for these patients and can facilitate treatment adjustments or allow for early salvage therapies.

PURPOSE

In this work, a novel deep learning architecture is introduced to predict the outcome of local control/failure in brain metastasis treated with stereotactic radiation therapy using treatment-planning magnetic resonance imaging (MRI) and standard clinical attributes.

METHODS

At the core of the proposed architecture is an InceptionResentV2 network to extract distinct features from each MRI slice for local outcome prediction. A recurrent or transformer network is integrated into the architecture to incorporate spatial dependencies between MRI slices into the predictive modeling. A visualization method based on prediction difference analysis is coupled with the deep learning model to illustrate how different regions of each lesion on MRI contribute to the model's prediction. The model was trained and optimized using the data acquired from 99 patients (116 lesions) and evaluated on an independent test set of 25 patients (40 lesions).

RESULTS

The results demonstrate the promising potential of the MRI deep learning features for outcome prediction, outperforming standard clinical variables. The prediction model with only clinical variables demonstrated an area under the receiver operating characteristic curve (AUC) of 0.68. The MRI deep learning models resulted in AUCs in the range of 0.72 to 0.83 depending on the mechanism to integrate information from MRI slices of each lesion. The best prediction performance (AUC = 0.86) was associated with the model that combined the MRI deep learning features with clinical variables and incorporated the inter-slice dependencies using a long short-term memory recurrent network. The visualization results highlighted the importance of tumor/lesion margins in local outcome prediction for brain metastasis.

CONCLUSIONS

The promising results of this study show the possibility of early prediction of radiotherapy outcome for brain metastasis via deep learning of MRI and clinical attributes at pre-treatment and encourage future studies on larger groups of patients treated with other radiotherapy modalities.

Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy

In this study, a novel deep learning-based methodology was investigated to predict breast cancer response to neo-adjuvant chemotherapy (NAC) using the quantitative ultrasound (QUS) multi-parametric imaging at pre-treatment. QUS multi-parametric images of breast tumors were generated using the data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for NAC followed by surgery. The ground truth response to NAC was identified for each patient after the surgery using the standard clinical and pathological criteria. Two deep convolutional neural network (DCNN) architectures including the residual network and residual attention network (RAN) were explored for extracting optimal feature maps from the parametric images, with a fully connected network for response prediction. In different experiments, the features maps were derived from the tumor core only, as well as the core and its margin. Evaluation results on an independent test set demonstrate that the developed model with the RAN architecture to extract feature maps from the expanded parametric images of the tumor core and margin had the best performance in response prediction with an accuracy of 88% and an area under the receiver operating characteristic curve of 0.86. Ten-year survival analyses indicate statistically significant differences between the survival of the responders and non-responders identified based on the model prediction at pre-treatment and the standard criteria at post-treatment. The results of this study demonstrate the promising capability of DCNNs with attention mechanisms in predicting breast cancer response to NAC prior to the start of treatment using QUS multi-parametric images.

Focused Ultrasound Stimulation of Microbubbles in Combination With Radiotherapy for Acute Damage of Breast Cancer Xenograft Model

Objective: Several studies have focused on the use of ultrasound-stimulated microbubbles (USMB) to induce vascular damage in order to enhance tumor response to radiation. Methods: In this study, power Doppler imaging was used along with immunohistochemistry to investigate the effects of combining radiation therapy (XRT) and USMB using an ultrasound-guided focused ultrasound (FUS) therapy system in a breast cancer xenograft model. Specifically, MDA-MB-231 breast cancer xenograft tumors were induced in severe combined immuno-deficient female mice. The mice were treated with FUS alone, ultrasound and microbubbles (FUS  +  MB) alone, 8 Gy XRT alone, or a combined treatment consisting of ultrasound, microbubbles, and XRT (FUS  +  MB  +  XRT). Power Doppler imaging was conducted before and 24 h after treatment, at which time mice were sacrificed and tumors assessed histologically. The immunohistochemical analysis included terminal deoxynucleotidyl transferase dUTP nick end labeling, hematoxylin and eosin, cluster of differentiation-31 (CD31), Ki-67, carbonic anhydrase (CA-9), and ceramide labeling. Results: Tumors receiving treatment of FUS  +  MB combined with XRT demonstrated significant increase in cell death (p  =  0.0006) compared to control group. Furthermore, CD31 and Power Doppler analysis revealed reduced tumor vascularization with combined treatment indicating ( P < .0001) and ( P  =  .0001), respectively compared to the control group. Additionally, lesser number of proliferating cells with enhanced tumor hypoxia, and ceramide content were also reported in group receiving a treatment of FUS  +  MB  +  XRT. Conclusion: The study results demonstrate that the combination of USMB with XRT enhances treatment outcomes.

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.

In vivo assessment of prostate cancer response using quantitative ultrasound characterization of ultrasonic scattering properties

Background

The study here investigated quantitative ultrasound (QUS) parameters to assess tumour response to ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) treatment in vivo. Mice bearing prostate cancer xenografts were exposed to various treatment conditions including 1% (v/v) Definity microbubbles stimulated at ultrasound pressures 246 kPa and 570 kPa and HT duration of 0, 10, 40, and 50 min. Ultrasound radiofrequency (RF) data were collected using an ultrasound transducer with a central frequency of 25 MHz. QUS parameters based on form factor models were used as potential biomarkers of cell death in prostate cancer xenografts.

Results

The average acoustic concentration (AAC) parameter from spherical gaussian and the fluid-filled spherical models were the most efficient imaging biomarker of cell death. Statistical significant increases of AAC were found in the combined treatment groups: 246 kPa + 40 min, 246 kPa + 50 min, and 570 kPa + 50 min, in comparison with control tumours (0 kPa + 0 min). Changes in AAC correlates strongly (r² = 0.62) with cell death fraction quantified from the histopathological analysis.

Conclusion

Scattering property estimates from spherical gaussian and fluid-filled spherical models are useful imaging biomarkers for assessing tumour response to treatment. Our observation of changes in AAC from high ultrasound frequencies was consistent with previous findings where parameters related to the backscatter intensity (AAC) increased with cell death.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08706-7.

Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment

The efficacy of quantitative ultrasound (QUS) multi-parametric imaging in conjunction with unsupervised classification algorithms was investigated for the first time in characterizing intra-tumor regions to predict breast tumor response to chemotherapy before the start of treatment. QUS multi-parametric images of breast tumors were generated using the ultrasound radiofrequency data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for neo-adjuvant chemotherapy followed by surgery. A hidden Markov random field (HMRF) expectation maximization (EM) algorithm was applied to identify distinct intra-tumor regions on QUS multi-parametric images. Several features were extracted from the segmented intra-tumor regions and tumor margin on different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker consisting of four features for response prediction. Evaluation results on an independent test set indicated that the developed biomarker coupled with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patient at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In comparison, the biomarkers consisted of the features derived from the entire tumor core (without consideration of the intra-tumor regions), and the entire tumor core and the tumor margin could predict the treatment response of patients with an accuracy of 74.5% and 76.4%, and an AUC of 0.79 and 0.76, respectively. Standard clinical features could predict the therapy response with an accuracy of 69.1% and an AUC of 0.6. Long-term survival analyses indicated that the patients predicted by the developed model as responders had a significantly better survival compared to the non-responders. Similar findings were observed for the two response cohorts identified at post-treatment based on standard clinical and pathological criteria. The results obtained in this study demonstrated the potential of QUS multi-parametric imaging integrated with unsupervised learning methods in identifying distinct intra-tumor regions in breast cancer to characterize its responsiveness to chemotherapy prior to the start of treatment.

Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning

•

Textural and second derivative textural features of CT images can be used in conjunction with machine learning models to predict breast cancer response to chemotherapy prior to the start of treatment.

•

The proposed predictive model separates the patients at pre-treatment into two cohorts (responders/non-responders) with significantly different survival.

•

The proposed methodology is a step forward towards the precision oncology paradigm for breast cancer patients.

Although neoadjuvant chemotherapy (NAC) is a crucial component of treatment for locally advanced breast cancer (LABC), only about 70% of patients respond to it. Effective adjustment of NAC for individual patients can significantly improve survival rates of those resistant to standard regimens. Thus, the early prediction of NAC outcome is of great importance in facilitating a personalized paradigm for breast cancer therapeutics. In this study, quantitative computed tomography (qCT) parametric imaging in conjunction with machine learning techniques were investigated to predict LABC tumor response to NAC. Textural and second derivative textural (SDT) features of CT images of 72 patients diagnosed with LABC were analysed before the initiation of NAC to quantify intra-tumor heterogeneity. These quantitative features were processed through a correlation-based feature reduction followed by a sequential feature selection with a bootstrap 0.632+ area under the receiver operating characteristic (ROC) curve (AUC0.632+) criterion. The best feature subset consisted of a combination of one textural and three SDT features. Using these features, an AdaBoost decision tree could predict the patient response with a cross-validated AUC0.632+ accuracy, sensitivity and specificity of 0.88, 85%, 88% and 75%, respectively. This study demonstrates, for the first time, that a combination of textural and SDT features of CT images can be used to predict breast cancer response NAC prior to the start of treatment which can potentially facilitate early therapy adjustments.

A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning

Response to Neoadjuvant chemotherapy (NAC) has demonstrated a high correlation to survival in locally advanced breast cancer (LABC) patients. An early prediction of responsiveness to NAC could facilitate treatment adjustments on an individual patient basis that would be expected to improve treatment outcomes and patient survival. This study investigated, for the first time, the efficacy of quantitative computed tomography (qCT) parametric imaging to characterize intra-tumour heterogeneity and its application in predicting tumour response to NAC in LABC patients. Textural analyses were performed on CT images acquired from 72 patients before the start of chemotherapy to determine quantitative features of intra-tumour heterogeneity. The best feature subset for response prediction was selected through a sequential feature selection with bootstrap 0.632 + area under the receiver operating characteristic (ROC) curve (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{A}\mathrm{U}\mathrm{C}}_{0.632+}$$\end{document}AUC0.632+) as a performance criterion. Several classifiers were evaluated for response prediction using the selected feature subset. Amongst the applied classifiers an Adaboost decision tree provided the best results with cross-validated \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{A}\mathrm{U}\mathrm{C}}_{0.632+}$$\end{document}AUC0.632+, accuracy, sensitivity and specificity of 0.89, 84%, 80% and 88%, respectively. The promising results obtained in this study demonstrate the potential of the proposed biomarkers to be used as predictors of LABC tumour response to NAC prior to the start of treatment.

Machine Learning-Based A Priori Chemotherapy Response Prediction in Breast Cancer Patients using Textural CT Biomarkers. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:1250-1253. [PMID: 33018214 DOI: 10.1109/embc44109.2020.9176099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Early prediction of cancer response to neoadjuvant chemotherapy (NAC) could permit personalized treatment adjustments for patients, which would improve treatment outcomes and patient survival. For the first time, the efficiency of quantitative computed tomography (qCT) textural and second derivative of textural (SDT) features were investigated and compared in this study. It was demonstrated that intra-tumour heterogeneity can be probed through these biomarkers and used as chemotherapy tumour response predictors in breast cancer patients prior to the start of treatment. These features were used to develop a machine learning approach which provided promising results with cross-validated AUC0.632+, accuracy, sensitivity and specificity of 0.86, 81%, 74% and 88%, respectively.Clinical Relevance- The results obtained in this study demonstrate the potential of textural CT biomarkers as response predictors of standard NAC before treatment initiation.

Mean Scatterer Spacing Estimation Using Cepstrum-Based Continuous Wavelet Transform

The goal of this study was to develop an ultrasound (US) scatterer spacing estimation method using an enhanced cepstral analysis based on continuous wavelet transforms (CWTs). Simulations of backscattering media containing periodic and quasi-periodic scatterers were carried out to test the developed algorithm. Experimental data from HT-29 pellets and in vivo PC3 tumors were then used to estimate the mean scatterer spacing. For simulated media containing quasi-periodic scatterers at 1-mm and 100- [Formula: see text] spacing with 5% positional variation, the developed algorithm yielded a spacing estimation error of ~1% for 25- and 55-MHz US pulses. The mean scatterer spacing of HT-29 cell pellets (31.97 [Formula: see text]) was within 3% of the spacing obtained from histology and agreed with the predicted spacing from simulations based on the same pellets for both frequencies. The agreement extended to in vivo PC3 tumors estimation of the spacing with a variance of 1.68% between the spacing derived from the tumor histology and the application of the CWT to the experimental results. The developed technique outperformed the traditional cepstral methods as it can detect nonprominent peaks from quasi-random scatterer configurations. This work can be potentially used to detect morphological tissue changes during normal development or disease treatment.

Quantitative Ultrasound Monitoring of Breast Tumour Response to Neoadjuvant Chemotherapy: Comparison of Results Among Clinical Scanners

Quantitative ultrasound (QUS) techniques have been demonstrated to detect cell death in vitro and in vivo. Recently, multi-feature classification models have been incorporated into QUS texture-feature analysis methods to increase further the sensitivity and specificity of detecting treatment response in locally advanced breast cancer patients. To effectively incorporate these analytic methods into clinical applications, QUS and texture-feature estimations should be independent of data acquisition systems. The study here investigated the consistencies of QUS and texture-feature estimation techniques relative to several factors. These included the ultrasound system properties, the effects of tissue heterogeneity and the effects of these factors on the monitoring of response to neoadjuvant chemotherapy. Specifically, tumour-response-detection performance based on QUS and texture parameters using two clinical ultrasound systems was compared. Observed variations in data between the systems were small and the results exhibited good agreement in tumour response predictions obtained from both ultrasound systems. The results obtained in this study suggest that tissue heterogeneity was a dominant feature in the parameters measured with the two different ultrasound systems; whereas differences in ultrasound system beam properties only exhibited a minor impact on texture features. The McNemar statistical test performed on tumour response prediction results from the two systems did not reveal significant differences. Overall, the results in this study demonstrate the potential to achieve reliable and consistent QUS and texture-based analyses across different ultrasound imaging platforms.

Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis

About 20-40% of cancer patients develop brain metastases, causing significant morbidity and mortality. Stereotactic radiation treatment is an established option that delivers high dose radiation to the target while sparing the surrounding normal tissue. However, up to 20% of metastatic brain tumours progress despite stereotactic treatment, and it can take months before it is evident on follow-up imaging. An early predictor of radiation therapy outcome in terms of tumour local failure (LF) is crucial, and can facilitate treatment adjustments or allow for early salvage treatment. In this study, an MR-based radiomics framework was proposed to derive and investigate quantitative MRI (qMRI) biomarkers for the outcome of LF in brain metastasis patients treated with hypo-fractionated stereotactic radiation therapy (SRT). The qMRI biomarkers were constructed through a multi-step feature extraction/reduction/selection framework using the conventional MR imaging data acquired from 100 patients (133 lesions), and were applied in conjunction with machine learning techniques for outcome prediction and risk assessment. The results indicated that the majority of the features in the optimal qMRI biomarkers characterize the heterogeneity in the surrounding regions of tumour including edema and tumour/lesion margins. The optimal qMRI biomarker consisted of five features that predict the outcome of LF with an area under the curve (AUC) of 0.79, and a cross-validated sensitivity and specificity of 81% and 79%, respectively. The Kaplan-Meier analyses showed a statistically significant difference in local control (p-value < 0.0001) and overall survival (p = 0.01). Findings from this study are a step towards using qMRI for early prediction of local failure in brain metastasis patients treated with SRT. This may facilitate early adjustments in treatment, such as surgical resection or salvage radiation, that can potentially improve treatment outcomes. Investigations on larger cohorts of patients are, however, required for further validation of the technique.

Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models

PURPOSE

The purpose of this study was to develop computational algorithms to best determine tumor responses early after the start of neoadjuvant chemotherapy, based on quantitative ultrasound (QUS) and textural analysis in patients with locally advanced breast cancer (LABC).

METHODS

A total of 100 LABC patients treated with neoadjuvant chemotherapy were included in this study. Breast tumors were scanned with a clinical ultrasound system prior to treatment, during the first, fourth and eighth weeks of treatment, and prior to surgery. QUS parameters were calculated from ultrasound radio frequency data within tumor regions. Texture features were extracted from each QUS parametric map. Patients were classified into two groups based on identified clinical/pathological response: responders and non-responders. In order to differentiate treatment responders, three multi-feature response classification algorithms, namely a linear discriminant, a k-nearest-neighbor and a nonlinear support vector machine classifier were compared.

RESULTS

All algorithms distinguished responders and non-responders with accuracies ranging between 68% and 92%. In particular, support vector machine performed the best in differentiating responders from non-responders with accuracies of 78%, 90% and 92% at weeks 1, 4 and 8 after the start of treatment, respectively. The most relevant features in separating the two response groups at early stages (weeks 1and 4) were texture features and at a later stage (week 8) were mean QUS parameters, particularly ultrasound backscatter intensity-based parameters.

CONCLUSION

An early stage treatment response prediction model developed by quantitative ultrasound and texture analysis combined with modern computational methods permits offering effective alternatives to standard treatment for refractory patients.

Monitoring breast cancer response to neoadjuvant chemotherapy with ultrasound signal statistics and integrated backscatter

Background

Neoadjuvant 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.

Methods

The 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.

Results

Statistically 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.

Conclusions

Quantitative 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.

Ultrasound echogenicity reveals the response of breast cancer to chemotherapy

PURPOSE

To evaluate the ultrasound (US) response in patients with breast cancer (BC) during neoadjuvant chemotherapy (NAC).

METHODS

Prospective US analysis was performed on 19 malignant tumors prior to NAC treatment and 7 days after each first four courses of NAC in 13 patients (median age = 57 years). Echogenicity, size, vascularity, and sonoelastography were measured and compared with posttreatment scores of residual cancers burden.

RESULTS

Changes in the echogenicity of tumors after 3 courses of NAC had the most statistically strong correlation with the percentage of residual malignant cells used in histopathology to assess the response to treatment (odds ratio = 60, p < 0.05). Changes in lesion size and elasticity were also significant (p < 0.05).

CONCLUSIONS

There is a statistically significant relationship between breast tumors' echogenicity in US, neoplasm size, and stiffness and the response to NAC. In particular, our results show that the change in tumor echogenicity could predict a pathological response with satisfactory accuracy and may be considered in NAC monitoring.

Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features

Background

Pathological response of breast cancer to chemotherapy is a prognostic indicator for long-term disease free and overall survival. Responses of locally advanced breast cancer in the neoadjuvant chemotherapy (NAC) settings are often variable, and the prediction of response is imperfect. The purpose of this study was to detect primary tumor responses early after the start of neoadjuvant chemotherapy using quantitative ultrasound (QUS), textural analysis and molecular features in patients with locally advanced breast cancer.

Methods

The study included ninety six patients treated with neoadjuvant chemotherapy. Breast tumors were scanned with a clinical ultrasound system prior to chemotherapy treatment, during the first, fourth and eighth week of treatment, and prior to surgery. Quantitative ultrasound parameters and scatterer-based features were calculated from ultrasound radio frequency (RF) data within tumor regions of interest. Additionally, texture features were extracted from QUS parametric maps. Prior to therapy, all patients underwent a core needle biopsy and histological subtypes and biomarker ER, PR, and HER2 status were determined. Patients were classified into three treatment response groups based on combination of clinical and pathological analyses: complete responders (CR), partial responders (PR), and non-responders (NR). Response classifications from QUS parameters, receptors status and pathological were compared. Discriminant analysis was performed on extracted parameters using a support vector machine classifier to categorize subjects into CR, PR, and NR groups at all scan times.

Results

Of the 96 patients, the number of CR, PR and NR patients were 21, 52, and 23, respectively. The best prediction of treatment response was achieved with the combination mean QUS values, texture and molecular features with accuracies of 78%, 86% and 83% at weeks 1, 4, and 8, after treatment respectively. Mean QUS parameters or clinical receptors status alone predicted the three response groups with accuracies less than 60% at all scan time points. Recurrence free survival (RFS) of response groups determined based on combined features followed similar trend as determined based on clinical and pathology.

Conclusions

This work demonstrates the potential of using QUS, texture and molecular features for predicting the response of primary breast tumors to chemotherapy early, and guiding the treatment planning of refractory patients.

Review of quantitative multiscale imaging of breast cancer

Breast cancer is the most common cancer among women worldwide and ranks second in terms of overall cancer deaths. One of the difficulties associated with treating breast cancer is that it is a heterogeneous disease with variations in benign and pathologic tissue composition, which contributes to disease development, progression, and treatment response. Many of these phenotypes are uncharacterized and their presence is difficult to detect, in part due to the sparsity of methods to correlate information between the cellular microscale and the whole-breast macroscale. Quantitative multiscale imaging of the breast is an emerging field concerned with the development of imaging technology that can characterize anatomic, functional, and molecular information across different resolutions and fields of view. It involves a diverse collection of imaging modalities, which touch large sections of the breast imaging research community. Prospective studies have shown promising results, but there are several challenges, ranging from basic physics and engineering to data processing and quantification, that must be met to bring the field to maturity. This paper presents some of the challenges that investigators face, reviews currently used multiscale imaging methods for preclinical imaging, and discusses the potential of these methods for clinical breast imaging.

Tumour Vascular Shutdown and Cell Death Following Ultrasound-Microbubble Enhanced Radiation Therapy

High-dose radiotherapy effects are regulated by acute tumour endothelial cell death followed by rapid tumour cell death instead of canonical DNA break damage. Pre-treatment with ultrasound-stimulated microbubbles (USMB) has enabled higher-dose radiation effects with conventional radiation doses. This study aimed to confirm acute and longitudinal relationships between vascular shutdown and tumour cell death following radiation and USMB in a wild type murine fibrosarcoma model using in vivo imaging.

Methods: Tumour xenografts were treated with single radiation doses of 2 or 8 Gy alone, or in combination with low-/high-concentration USMB. Vascular changes and tumour cell death were evaluated at 3, 24 and 72 h following therapy, using high-frequency 3D power Doppler and quantitative ultrasound spectroscopy (QUS) methods, respectively. Staining using in situ end labelling (ISEL) and cluster of differentiation 31 (CD31) of tumour sections were used to assess cell death and vascular distributions, respectively, as gold standard histological methods.

Results: Results indicated a decrease in the power Doppler signal of up to 50%, and an increase of more than 5 dBr in cell-death linked QUS parameters at 24 h for tumours treated with combined USMB and radiotherapy. Power Doppler and quantitative ultrasound results were significantly correlated with CD31 and ISEL staining results (p < 0.05), respectively. Moreover, a relationship was found between ultrasound power Doppler and QUS results, as well as between micro-vascular densities (CD31) and the percentage of cell death (ISEL) (R² 0.5-0.9).

Conclusions: This study demonstrated, for the first time, the link between acute vascular shutdown and acute tumour cell death using in vivo longitudinal imaging, contributing to the development of theoretical models that incorporate vascular effects in radiation therapy. Overall, this study paves the way for theranostic use of ultrasound in radiation oncology as a diagnostic modality to characterize vascular and tumour response effects simultaneously, as well as a therapeutic modality to complement radiation therapy.

Deep reinforcement learning for automated radiation adaptation in lung cancer

PURPOSE

To investigate deep reinforcement learning (DRL) based on historical treatment plans for developing automated radiation adaptation protocols for nonsmall cell lung cancer (NSCLC) patients that aim to maximize tumor local control at reduced rates of radiation pneumonitis grade 2 (RP2).

METHODS

In a retrospective population of 114 NSCLC patients who received radiotherapy, a three-component neural networks framework was developed for deep reinforcement learning (DRL) of dose fractionation adaptation. Large-scale patient characteristics included clinical, genetic, and imaging radiomics features in addition to tumor and lung dosimetric variables. First, a generative adversarial network (GAN) was employed to learn patient population characteristics necessary for DRL training from a relatively limited sample size. Second, a radiotherapy artificial environment (RAE) was reconstructed by a deep neural network (DNN) utilizing both original and synthetic data (by GAN) to estimate the transition probabilities for adaptation of personalized radiotherapy patients' treatment courses. Third, a deep Q-network (DQN) was applied to the RAE for choosing the optimal dose in a response-adapted treatment setting. This multicomponent reinforcement learning approach was benchmarked against real clinical decisions that were applied in an adaptive dose escalation clinical protocol. In which, 34 patients were treated based on avid PET signal in the tumor and constrained by a 17.2% normal tissue complication probability (NTCP) limit for RP2. The uncomplicated cure probability (P+) was used as a baseline reward function in the DRL.

RESULTS

Taking our adaptive dose escalation protocol as a blueprint for the proposed DRL (GAN + RAE + DQN) architecture, we obtained an automated dose adaptation estimate for use at ∼2/3 of the way into the radiotherapy treatment course. By letting the DQN component freely control the estimated adaptive dose per fraction (ranging from 1-5 Gy), the DRL automatically favored dose escalation/de-escalation between 1.5 and 3.8 Gy, a range similar to that used in the clinical protocol. The same DQN yielded two patterns of dose escalation for the 34 test patients, but with different reward variants. First, using the baseline P+ reward function, individual adaptive fraction doses of the DQN had similar tendencies to the clinical data with an RMSE = 0.76 Gy; but adaptations suggested by the DQN were generally lower in magnitude (less aggressive). Second, by adjusting the P+ reward function with higher emphasis on mitigating local failure, better matching of doses between the DQN and the clinical protocol was achieved with an RMSE = 0.5 Gy. Moreover, the decisions selected by the DQN seemed to have better concordance with patients eventual outcomes. In comparison, the traditional temporal difference (TD) algorithm for reinforcement learning yielded an RMSE = 3.3 Gy due to numerical instabilities and lack of sufficient learning.

CONCLUSION

We demonstrated that automated dose adaptation by DRL is a feasible and a promising approach for achieving similar results to those chosen by clinicians. The process may require customization of the reward function if individual cases were to be considered. However, development of this framework into a fully credible autonomous system for clinical decision support would require further validation on larger multi-institutional datasets.

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic.

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis.

Low-frequency ultrasound radiosensitization and therapy response monitoring of tumors: an in vivo study

A new framework has been introduced in this paper for tumor radiosensitization and therapy response monitoring using low-frequency ultrasound. Human fibrosarcoma xenografts grown in severe combined immunodeficiency (SCID) mice (n = 108) were treated using ultrasound-stimulated microbubbles at various concentration and exposed to different doses of radiation. Low-frequency ultrasound radiofrequency (RF) data were acquired from tumors prior to and at different times after treatment. Quantitative ultrasound (QUS) techniques were applied to generate spectral parametric maps of tumors. Textural analysis were performed to quantify spatial heterogeneities within QUS parametric maps. A hybrid model was developed using multiple regression analysis to predict extent of histological tumor cell death non-invasively based on QUS spectral and textural biomarkers. Results of immunohistochemistry on excised tumor sections demonstrated increases in cell death with higher concentration of microbubbles and radiation dose. Quantitative ultrasound results indicated changes that paralleled increases in histological cell death. Specifically, the hybrid QUS biomarker demonstrated a good correlation with extent of tumor cell death observed from immunohistochemistry. A linear discriminant analysis applied in conjunction with the receiver operating characteristic (ROC) curve analysis indicated that the hybrid QUS biomarker can classify tumor cell death fractions with an area under the curve of 91.2. The results obtained in this research suggest that low-frequency ultrasound can concurrently be used to enhance radiation therapy and evaluate tumor response to treatment.

Computer aided prognosis for cell death categorization and prediction in vivo using quantitative ultrasound and machine learning techniques

PURPOSE

At present, a one-size-fits-all approach is typically used for cancer therapy in patients. This is mainly because there is no current imaging-based clinical standard for the early assessment and monitoring of cancer treatment response. Here, the authors have developed, for the first time, a complete computer-aided-prognosis (CAP) system based on multiparametric quantitative ultrasound (QUS) spectroscopy methods in association with texture descriptors and advanced machine learning techniques. This system was used to noninvasively categorize and predict cell death levels in fibrosarcoma mouse tumors treated using ultrasound-stimulated microbubbles as novel endothelial-cell radiosensitizers.

METHODS

Sarcoma xenograft tumor-bearing mice were treated using ultrasound-stimulated microbubbles, alone or in combination with x-ray radiation therapy, as a new antivascular treatment. Therapy effects were assessed at 2-3, 24, and 72 h after treatment using a high-frequency ultrasound. Two-dimensional spectral parametric maps were generated using the power spectra of the raw radiofrequency echo signal. Subsequently, the distances between "pretreatment" and "post-treatment" scans were computed as an indication of treatment efficacy, using a kernel-based metric on textural features extracted from 2D parametric maps. A supervised learning paradigm was used to either categorize cell death levels as low, medium, or high using a classifier, or to "continuously" predict the levels of cell death using a regressor.

RESULTS

The developed CAP system performed at a high level for the classification of cell death levels. The area under curve of the receiver operating characteristic was 0.87 for the classification of cell death levels to both low/medium and medium/high levels. Moreover, the prediction of cell death levels using the proposed CAP system achieved a good correlation (r = 0.68,  p < 0.001) with histological cell death levels as the ground truth. A statistical test of significance between individual treatment groups with the corresponding control group demonstrated that the predicted levels indicated the same significant changes in cell death as those indicated by the ground-truth levels.

CONCLUSIONS

The technology developed in this study addresses a gap in the current standard of care by introducing a quality control step that generates potentially actionable metrics needed to enhance treatment decision-making. The study establishes a noninvasive framework for quantifying levels of cancer treatment response developed preclinically in tumors using QUS imaging in conjunction with machine learning techniques. The framework can potentially facilitate the detection of refractory responses in patients to a certain cancer treatment early on in the course of therapy to enable switching to more efficacious treatments.

High-frequency ultrasound detection of cell death: Spectral differentiation of different forms of cell death in vitro

High frequency quantitative ultrasound techniques were investigated to characterize different forms of cell death in vitro. Suspension-grown acute myeloid leukemia cells were treated to cause apoptosis, oncosis, mitotic arrest, and heat-induced death. Samples were scanned with 20 and 40 MHz ultrasound and assessed histologically in terms of cellular structure. Frequency-domain analysis of 20 MHz ultrasound data demonstrated midband fit changes of 6.0 ± 0.7 dBr, 6.2 ± 1.8 dBr, 4.0 ± 1.0 dBr and −4.6 ± 1.7 dBr after 48-hour cisplatinum-induced apoptosis, 48-hour oncotic decay, 36-hour colchicine-induced mitotic arrest, and heat treatment compared to control, respectively. Trends from 40 MHz ultrasound were similar. Spectral slope changes obtained from 40 MHz ultrasound data were reflective of alterations in cell and nucleus size. Chromatin pyknosis or lysis trends suggested that the density of nuclear material may be responsible for observed changes in ultrasound backscatter. Flow cytometry analysis confirmed the modes of cell death and supported midband fit trends in ultrasound data. Scatterer-size and concentration estimates obtained from a fluid-filled sphere form factor model further corresponded with spectral analysis and histology. Results indicate quantitative ultrasound spectral analysis may be used for probing anti-cancer response and distinguishing various modes of cell death in vitro.

Early detection of chemotherapy-refractory patients by monitoring textural alterations in diffuse optical spectroscopic images

PURPOSE

Changes in textural characteristics of diffuse optical spectroscopic (DOS) functional images, accompanied by alterations in their mean values, are demonstrated here for the first time as early surrogates of ultimate treatment response in locally advanced breast cancer (LABC) patients receiving neoadjuvant chemotherapy (NAC). NAC, as a standard component of treatment for LABC patient, induces measurable heterogeneous changes in tumor metabolism which were evaluated using DOS-based metabolic maps. This study characterizes such inhomogeneous nature of response development, by determining alterations in textural properties of DOS images apparent at early stages of therapy, followed later by gross changes in mean values of these functional metabolic maps.

METHODS

Twelve LABC patients undergoing NAC were scanned before and at four times after treatment initiation, and tomographic DOS images were reconstructed at each time. Ultimate responses of patients were determined clinically and pathologically, based on a reduction in tumor size and assessment of residual tumor cellularity. The mean-value parameters and textural features were extracted from volumetric DOS images for several functional and metabolic parameters prior to the treatment initiation. Changes in these DOS-based biomarkers were also monitored over the course of treatment. The measured biomarkers were applied to differentiate patient responses noninvasively and compared to clinical and pathologic responses.

RESULTS

Responding and nonresponding patients demonstrated different changes in DOS-based textural and mean-value parameters during chemotherapy. Whereas none of the biomarkers measured prior the start of therapy demonstrated a significant difference between the two patient populations, statistically significant differences were observed at week one after treatment initiation using the relative change in contrast/homogeneity of seven functional maps (0.001<p<0.049), and mean value of water content in tissue (p=0.010). The cross-validated sensitivity and specificity of these parameters at week one of therapy ranged between 80%-100% and 67%-100%, respectively. Higher levels of statistically significant differences were exhibited at week four after start of treatment, with cross-validated sensitivities and specificities ranging between 80% and 100% for three textural and three mean-value parameters. The combination of the textural and mean-value parameters in a "hybrid" profile could better separate the two patient populations early on during a course of treatment, with cross-validated sensitivities and specificities of up to 100% (p=0.001).

CONCLUSIONS

The results of this study suggest that alterations in textural characteristics of DOS images, in conjunction with changes in their mean values, can classify noninvasively the ultimate clinical and pathologic response of LABC patients to chemotherapy, as early as one week after start of their treatment. This provides a basis for using DOS imaging as a tool for therapy personalization.

Assessment of tumor response to radiation and vascular targeting therapy in mice using quantitative ultrasound spectroscopy

PURPOSE

It is now recognized that the tumor vasculature is in part responsible for regulating tumor responses to radiation therapy. However, the extent to which radiation-based vascular damage contributes to tumor cell death remains unknown. In this work, quantitative ultrasound spectroscopy (QUS) methods were used to investigate the acute responses of tumors to radiation-based vascular treatments.

METHODS

Tumor xenografts (MDA-MB-231) were treated with single radiation doses of 2 or 8 Gy alone, or in combination with pharmacological agents that modulate vascular radiosensitivity. The midband fit, the slope, and the 0-MHz intercept QUS parameters were obtained from a linear-regression fit to the averaged power spectrum of frequency-dependent ultrasound backscatter and were used to quantify acute tumor responses following treatment administration. Power spectrums were extracted from raw volumetric radio-frequency ultrasound data obtained before and 24 h following treatment administration. These parameters have previously been correlated to tumor cell death. Staining using in situ end labeling, carbonic anhydrase 9 and cluster of differentiation 31 of tumor sections were used to assess cell death, oxygenation, and vasculature distributions, respectively.

RESULTS

Results indicate a significant midband fit QUS parameter increases of 3.2 ± 0.3 dBr and 5.4 ± 0.5 dBr for tumors treated with 2 and 8 Gy radiation combined with the antiangiogenic agent Sunitinib, respectively. In contrast, tumors treated with radiation alone demonstrated a significant midband fit increase of 4.4 ± 0.3 dBr at 8 Gy only. Preadministration of basic fibroblast growth factor, an endothelial radioprotector, acted to minimize tumor response following single large doses of radiation. Immunohistochemical analysis was in general agreement with QUS findings; an R(2) of 0.9 was observed when quantified cell death was correlated with changes in midband fit.

CONCLUSIONS

Results from QUS analysis presented in this study confirm that acute tumor response is linked to a vascular effect following high doses of radiation therapy. Overall, this is in agreement with previous reports suggesting that acute tumor radiation response is regulated by a vascular-driven response. Data also suggest that Sunitinib may enhance tumor radiosensitivity through a vascular remodeling process, and that QUS may be sensitive to changes in tissue properties associated with vascular remodeling. Finally, the work also demonstrates the ability of QUS methods to monitor response to radiation-based vascular strategies.

Quantitative ultrasound imaging of therapy response in bladder cancer in vivo

Background and Aims

Quantitative ultrasound (QUS) was investigated to monitor bladder cancer treatment response in vivo and to evaluate tumor cell death from combined treatments using ultrasound-stimulated microbubbles and radiation therapy.

Methods

Tumor-bearing mice (n=45), with bladder cancer xenografts (HT- 1376) were exposed to 9 treatment conditions consisting of variable concentrations of ultrasound-stimulated Definity microbubbles [nil, low (1%), high (3%)], combined with single fractionated doses of radiation (0 Gy, 2 Gy, 8 Gy). High frequency (25 MHz) ultrasound was used to collect the raw radiofrequency (RF) data of the backscatter signal from tumors prior to, and 24 hours after treatment in order to obtain QUS parameters. The calculated QUS spectral parameters included the mid-band fit (MBF), and 0-MHz intercept (SI) using a linear regression analysis of the normalized power spectrum.

Results and Conclusions

There were maximal increases in QUS parameters following treatments with high concentration microbubbles combined with 8 Gy radiation: (ΔMBF = +6.41 ± 1.40 (±SD) dBr and SI= + 7.01 ± 1.20 (±SD) dBr. Histological data revealed increased cell death, and a reduction in nuclear size with treatments, which was mirrored by changes in quantitative ultrasound parameters. QUS demonstrated markers to detect treatment effects in bladder tumors in vivo.

High-frequency ultrasound analysis of post-mitotic arrest cell death

Non-invasive monitoring of cancer cell death would permit rapid feedback on treatment response. One technique showing such promise is quantitative ultrasound. High-frequency ultrasound spectral radiofrequency analysis was used to study cell death in breast cancer cell samples. Quantitative ultrasound parameters, including attenuation, spectral slope, spectral 0-MHz-intercept, midband fit, and fitted parameters displayed significant changes with paclitaxel-induced cell death, corresponding to observations of morphological changes seen in histology and electron microscopy. In particular, a decrease in spectral slope from 0.24±0.07 dB/MHz to 0.04±0.09 dB/MHz occurred over 24 hours of treatment time and was identified as an ultrasound parameter capable of differentiating post-mitotic arrest cell death from classical apoptosis. The formation of condensed chromatin aggregates of 1 micron or greater in size increased the number of intracellular scatterers, consistent with a hypothesis that nuclear material is a primary source of ultrasound scattering in dying cells. It was demonstrated that the midband fit quantitatively correlated to cell death index, with a Pearson R-squared value of 0.99 at p<0.01. These results suggest that high-frequency ultrasound can not only qualitatively assess the degree of cancer cell death, but may be used to quantify the efficacy of chemotherapeutic treatments.

Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer

A noninvasive computer-aided-theragnosis (CAT) system was developed for the early therapeutic cancer response assessment in patients with locally advanced breast cancer (LABC) treated with neoadjuvant chemotherapy. The proposed CAT system was based on multi-parametric quantitative ultrasound (QUS) spectroscopic methods in conjunction with advanced machine learning techniques. Specifically, a kernel-based metric named maximum mean discrepancy (MMD), a technique for learning from imbalanced data based on random undersampling, and supervised learning were investigated with response-monitoring data from LABC patients. The CAT system was tested on 56 patients using statistical significance tests and leave-one-subject-out classification techniques. Textural features using state-of-the-art local binary patterns (LBP), and gray-scale intensity features were extracted from the spectral parametric maps in the proposed CAT system. The system indicated significant differences in changes between the responding and non-responding patient populations as well as high accuracy, sensitivity, and specificity in discriminating between the two patient groups early after the start of treatment, i.e., on weeks 1 and 4 of several months of treatment. The proposed CAT system achieved an accuracy of 85%, 87%, and 90% on weeks 1, 4 and 8, respectively. The sensitivity and specificity of developed CAT system for the same times was 85%, 95%, 90% and 85%, 85%, 91%, respectively. The proposed CAT system thus establishes a noninvasive framework for monitoring cancer treatment response in tumors using clinical ultrasound imaging in conjunction with machine learning techniques. Such a framework can potentially facilitate the detection of refractory responses in patients to treatment early on during a course of therapy to enable possibly switching to more efficacious treatments.

Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound

Conventional medical imaging technologies, including ultrasound, have continued to improve over the years. For example, in oncology, medical imaging is characterized by high sensitivity, i.e., the ability to detect anomalous tissue features, but the ability to classify these tissue features from images often lacks specificity. As a result, a large number of biopsies of tissues with suspicious image findings are performed each year with a vast majority of these biopsies resulting in a negative finding. To improve specificity of cancer imaging, quantitative imaging techniques can play an important role. Conventional ultrasound B-mode imaging is mainly qualitative in nature. However, quantitative ultrasound (QUS) imaging can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound. QUS imaging can encompass a wide variety of techniques including spectral-based parameterization, elastography, shear wave imaging, flow estimation, and envelope statistics. Currently, spectral-based parameterization and envelope statistics are not available on most conventional clinical ultrasound machines. However, in recent years, QUS techniques involving spectral-based parameterization and envelope statistics have demonstrated success in many applications, providing additional diagnostic capabilities. Spectral-based techniques include the estimation of the backscatter coefficient (BSC), estimation of attenuation, and estimation of scatterer properties such as the correlation length associated with an effective scatterer diameter (ESD) and the effective acoustic concentration (EAC) of scatterers. Envelope statistics include the estimation of the number density of scatterers and quantification of coherent to incoherent signals produced from the tissue. Challenges for clinical application include correctly accounting for attenuation effects and transmission losses and implementation of QUS on clinical devices. Successful clinical and preclinical applications demonstrating the ability of QUS to improve medical diagnostics include characterization of the myocardium during the cardiac cycle, cancer detection, classification of solid tumors and lymph nodes, detection and quantification of fatty liver disease, and monitoring and assessment of therapy.

Quantification of Ultrasonic Scattering Properties of In Vivo Tumor Cell Death in Mouse Models of Breast Cancer

INTRODUCTION: Quantitative ultrasound parameters based on form factor models were investigated as potential biomarkers of cell death in breast tumor (MDA-231) xenografts treated with chemotherapy. METHODS: Ultrasound backscatter radiofrequency data were acquired from MDA-231 breast cancer tumor–bearing mice (n = 20) before and after the administration of chemotherapy drugs at two ultrasound frequencies: 7 MHz and 20 MHz. Radiofrequency spectral analysis involved estimating the backscatter coefficient from regions of interest in the center of the tumor, to which form factor models were fitted, resulting in estimates of average scatterer diameter and average acoustic concentration (AAC). RESULTS: The ∆AAC parameter extracted from the spherical Gaussian model was found to be the most effective cell death biomarker (at the lower frequency range, r² = 0.40). At both frequencies, AAC in the treated tumors increased significantly (P = .026 and .035 at low and high frequencies, respectively) 24 hours after treatment compared with control tumors. Furthermore, stepwise multiple linear regression analysis of the low-frequency data revealed that a multiparameter quantitative ultrasound model was strongly correlated to cell death determined histologically posttreatment (r² = 0.74). CONCLUSION: The Gaussian form factor model–based scattering parameters can potentially be used to track the extent of cell death at clinically relevant frequencies (7 MHz). The 20-MHz results agreed with previous findings in which parameters related to the backscatter intensity (i.e., AAC) increased with cell death. The findings suggested that, in addition to the backscatter coefficient parameter ∆AAC, biological features including tumor heterogeneity and initial tumor volume were important factors in the prediction of cell death response.

Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture

Early alterations in textural characteristics of quantitative ultrasound spectral parametric maps, in conjunction with changes in their mean values, are demonstrated here, for the first time, to be capable of predicting ultimate clinical/pathologic responses of breast cancer patients to chemotherapy. Mechanisms of cell death, induced by chemotherapy within tumor, introduce morphological alterations in cancerous cells, resulting in measurable changes in tissue echogenicity. We have demonstrated that the development of such changes is reflected in early alterations in textural characteristics of quantitative ultrasound spectral parametric maps, followed by consequent changes in their mean values. The spectral/textural biomarkers derived on this basis have been demonstrated as non-invasive surrogates of breast cancer chemotherapy response. Particularly, spectral biomarkers sensitive to the size and concentration of acoustic scatterers could predict treatment response of patients with up to 80% of sensitivity and specificity (p=0.050), after one week within 3-4 months of chemotherapy. However, textural biomarkers characterizing heterogeneities in distribution of acoustic scatterers, could differentiate between treatment responding and non-responding patients with up to 100% sensitivity and 93% specificity (p=0.002). Such early prediction permits offering effective alternatives to standard treatment, or switching to a salvage therapy, for refractory patients.

Quantitative ultrasound spectroscopic imaging for characterization of disease extent in prostate cancer patients

Three-dimensional quantitative ultrasound spectroscopic imaging of prostate was investigated clinically for the noninvasive detection and extent characterization of disease in cancer patients and compared to whole-mount, whole-gland histopathology of radical prostatectomy specimens. Fifteen patients with prostate cancer underwent a volumetric transrectal ultrasound scan before radical prostatectomy. Conventional-frequency (~5MHz) ultrasound images and radiofrequency data were collected from patients. Normalized power spectra were used as the basis of quantitative ultrasound spectroscopy. Specifically, color-coded parametric maps of 0-MHz intercept, midband fit, and spectral slope were computed and used to characterize prostate tissue in ultrasound images. Areas of cancer were identified in whole-mount histopathology specimens, and disease extent was correlated to that estimated from quantitative ultrasound parametric images. Midband fit and 0-MHz intercept parameters were found to be best associated with the presence of disease as located on histopathology whole-mount sections. Obtained results indicated a correlation between disease extent estimated noninvasively based on midband fit parametric images and that identified histopathologically on prostatectomy specimens, with an r(2) value of 0.71 (P<.0001). The 0-MHz intercept parameter demonstrated a lower level of correlation with histopathology. Spectral slope parametric maps offered no discrimination of disease. Multiple regression analysis produced a hybrid disease characterization model (r(2)=0.764, P<.05), implying that the midband fit biomarker had the greatest correlation with the histopathologic extent of disease. This work demonstrates that quantitative ultrasound spectroscopic imaging can be used for detecting prostate cancer and characterizing disease extent noninvasively, with corresponding gross three-dimensional histopathologic correlation.

Thermometry and ablation monitoring with ultrasound

In this review we present the current status of ultrasound thermometry and ablation monitoring, with emphasis on the diverse approaches published in the literature and with an eye on which methods are closest to clinical reality. It is hoped that this review will serve as a guide to the expansion of sonographic methods for treatment monitoring and thermometry since the last brief review in 2007.

Non-invasive evaluation of breast cancer response to chemotherapy using quantitative ultrasonic backscatter parameters

Tumor response to neoadjuvant chemotherapy in patients (n=30) with locally advanced breast cancer (LABC) was examined using quantitative ultrasound. Three ultrasound backscatter parameters, the integrated backscatter coefficient (IBC), average scatterer diameter (ASD), and average acoustic concentration (AAC), were estimated from tumors prior to treatment and at four times during neoadjuvant chemotherapy treatment (weeks 0, 1, 4, 8, and prior to surgery) and compared to ultimate clinical and pathological tumor responses. Results demonstrated that among all parameters, AAC was the best indicator of tumor response early after starting treatment. The AAC parameter increased substantially in treatment-responding patients as early as one week after treatment initiation, further increased at week 4, and attained a maximum at week 8. In contrast, the backscatter parameters from non-responders did not show any changes after treatment initiation. The two patient populations exhibited a statistically significant difference in changes of AAC (p<0.001) and ASD (p=0.023) over all treatment times examined. The best prediction of treatment response was achieved with the combination of AAC and ASD at week 4 (82% sensitivity, 100% specificity, and 86% accuracy) of 12-18 weeks of treatment. The survival of patients with responsive ultrasound parameters was higher than patients with non-responsive ultrasound parameters (35 ± 11 versus 27 ± 11 months, respectively, p=0.043). This study demonstrates that ultrasound parameters derived from the ultrasound backscattered power spectrum can potentially serve as non-invasive early measures of clinical tumor response to chemotherapy treatments.

Experimental evaluation of spectral-based quantitative ultrasound imaging using plane wave compounding

Quantitative ultrasound (QUS) based on backscatter coefficient (BSC) estimation has shown potential for tissue characterization. Beamforming using plane wave compounding has advantages for echographic, Doppler, and elastographic imaging; however, to date, plane wave compounding has not been experimentally evaluated for the purpose of BSC estimation. In this study, two BSC-derived parameters (i.e., the BSC midband fit and intercept) were estimated from experimental data obtained using compound plane wave beamforming. For comparison, QUS parameters were also estimated from data obtained using both fixed focus and dynamic receive beamforming. An ultrasound imaging system equipped with a 9-MHz center frequency, 64-element array was used to collect data up to a depth of 45 mm. Two gelatin phantoms with randomly distributed 20-μm inclusions with a homogeneous scatterer concentration and a two-region scatterer concentration were used for assessing the precision and lateral resolution of QUS imaging, respectively. The use of plane wave compounding resulted in accurate QUS estimation (i.e., bias in the BSC parameters of less than 2 dB) and relatively constant lateral resolution (i.e., BSC midband fit 10% to 90% rise distance ranging between 1.0 and 1.5 mm) throughout a 45 mm field of view. Although both fixed focus and dynamic receive beamforming provided the same performance around the focal depth, the reduction in SNR away from the focus resulted in a reduced field of view in the homogeneous phantom (i.e., only 28 mm). The lateral resolution also degraded away from the focus, with up to a 2-fold and 10-fold increase in the rise distance at 20 mm beyond the focal depth for dynamic receive and fixed focus beamforming, respectively. These results suggest that plane wave compounding has the potential to improve the performance of spectral-based quantitative ultrasound over other conventional beamforming strategies.

Categorizing extent of tumor cell death response to cancer therapy using quantitative ultrasound spectroscopy and maximum mean discrepancy

Quantitative ultrasound (QUS) spectroscopic techniques in conjunction with maximum mean discrepancy (MMD) have been proposed to detect, and to classify noninvasively the levels of cell death in response to cancer therapy administration in tumor models. Evaluation of xenograft tumor responses to cancer treatments were carried out using conventional-frequency ultrasound at different times after chemotherapy exposure. Ultrasound data were analyzed using spectroscopic techniques and multi-parametric QUS spectral maps were generated. MMD was applied as a distance criterion, measuring alterations in each tumor in response to chemotherapy, and the extent of cell death was classified into less/more than 20% and 40% categories. Statistically significant differences were observed between "pre-" and "post-treatment" groups at different times after chemotherapy exposure, suggesting a high capability of proposed framework for detecting tumor response noninvasively. Promising results were also obtained for categorizing the extent of cell death response in each tumor using the proposed framework, with gold standard histological quantification of cell death as ground truth. The best classification results were obtained using MMD when applied on histograms of QUS parametric maps. In this case, classification accuracies of 84.7% and 88.2% were achieved for categorizing extent of tumor cell death into less/more than 20% and 40%, respectively.