Imaging innovations for cancer therapy response monitoring

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.

Transfer learning of pre-treatment quantitative ultrasound multi-parametric images for the prediction of breast cancer response to neoadjuvant chemotherapy

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

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.

Use of photoacoustic imaging for monitoring vascular disrupting cancer treatments

Vascular disrupting agents disrupt tumor vessels, blocking the nutritional and oxygen supply tumors need to thrive. This is achieved by damaging the endothelium lining of blood vessels, resulting in red blood cells (RBCs) entering the tumor parenchyma. RBCs present in the extracellular matrix are exposed to external stressors resulting in biochemical and physiological changes. The detection of these changes can be used to monitor the efficacy of cancer treatments. Spectroscopic photoacoustic (PA) imaging is an ideal candidate for probing RBCs due to their high optical absorption relative to surrounding tissue. The goal of this work is to use PA imaging to monitor the efficacy of the vascular disrupting agent 5,6-Dimethylxanthenone-4-acetic acid (DMXAA) through quantitative analysis. Then, 4T1 breast cancer cells were injected subcutaneously into the left hind leg of eight BALB/c mice. After 10 days, half of the mice were treated with 15 mg/kg of DMXAA and the other half were injected with saline. All mice were imaged using the VevoLAZR X PA system before treatment, 24 and 72 hours after treatment. The imaging was done at six wavelengths and linear spectral unmixing was applied to the PA images to quantify three forms of hemoglobin (oxy, deoxy and met-hemoglobin). After imaging, tumors were histologically processed and H&E and TUNEL staining were used to detect the tissue damage induced by the DMXAA treatment. The total hemoglobin concentration remained unchanged after treatment for the saline treated mice. For DMXAA treated mice, a 10% increase of deoxyhemoglobin concentration was detected 24 hours after treatment and a 22.6% decrease in total hemoglobin concentration was observed by 72 hours. A decrease in the PA spectral slope parameters was measured 24 hours after treatment. This suggests that DMXAA induces vascular damage, causing red blood cells to extravasate. Furthermore, H&E staining of the tumor showed areas of bleeding with erythrocyte deposition. These observations are further supported by the increase in TUNEL staining in DMXAA treated tumors, revealing increased cell death due to vascular disruption. This study demonstrates the capability of PA imaging to monitor tumor vessel disruption by the vascular disrupting agent DMXAA.

A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning

This study investigated the effectiveness of pre-treatment quantitative MRI and clinical features along with machine learning techniques to predict local failure in patients with brain metastasis treated with hypo-fractionated stereotactic radiation therapy (SRT). The predictive models were developed using the data from 100 patients (141 lesions) and evaluated on an independent test set with data from 20 patients (30 lesions). Quantitative MRI radiomic features were derived from the treatment-planning contrast-enhanced T1w and T2-FLAIR images. A multi-phase feature reduction and selection procedure was applied to construct an optimal quantitative MRI biomarker for predicting therapy outcome. The performance of standard clinical features in therapy outcome prediction was evaluated using a similar procedure. Survival analyses were conducted to compare the long-term outcome of the two patient cohorts (local control/failure) identified based on prediction at pre-treatment, and standard clinical criteria at last patient follow-up after SRT. The developed quantitative MRI biomarker consists of four features with two features quantifying heterogeneity in the edema region, one feature characterizing intra-tumour heterogeneity, and one feature describing tumour morphology. The predictive models with the radiomic and clinical feature sets yielded an AUC of 0.87 and 0.62, respectively on the independent test set. Incorporating radiomic features into the clinical predictive model improved the AUC of the model by up to 16%, relatively. A statistically significant difference was observed in survival of the two patient cohorts identified at pre-treatment using the radiomics-based predictive model, and at post-treatment using the the RANO-BM criteria. Results of this study revealed a good potential for quantitative MRI radiomic features at pre-treatment in predicting local failure in relatively large brain metastases undergoing SRT, and is a step forward towards a precision oncology paradigm for brain metastasis.

Imaging Beyond Seeing: Early Prognosis of Cancer Treatment

Assessing cancer response to therapeutic interventions has been realized as an important course to early predict curative efficacy and treatment outcomes due to tumor heterogeneity. Compared to the traditional invasive tissue biopsy method, molecular imaging techniques have fundamentally revolutionized the ability to evaluate cancer response in a spatiotemporal manner. The past few years has witnessed a paradigm shift on the efforts from manufacturing functional molecular imaging probes for seeing a tumor to a vantage stage of interpreting the tumor response during different treatments. This review is to stand by the current development of advanced imaging technologies aiming to predict the treatment response in cancer therapy. Special interest is placed on the systems that are able to provide rapid and noninvasive assessment of pharmacokinetic drug fates (e.g., drug distribution, release, and activation) and tumor microenvironment heterogeneity (e.g., tumor cells, macrophages, dendritic cells (DCs), T cells, and inflammatory cells). The current status, practical significance, and future challenges of the emerging artificial intelligence (AI) technology and machine learning in the applications of medical imaging fields is overviewed. Ultimately, the authors hope that this review is timely to spur research interest in molecular imaging and precision medicine.

Monitoring the Response of Skin Melanoma Cell Line (A375) to Treatment with Vemurafenib: A Pilot In Vitro Optical Spectroscopic Study

Objective: The aim of this study was to investigate the feasibility of optical spectroscopy as a nondestructive approach in monitoring the skin melanoma cancer cell response to treatment. Background: Owing to the growing trend of personalized medicine, monitoring the treatment response individually is particularly crucial for optimizing cancer therapy efficiency. In the past decade, optical sensing, using diffuse reflectance spectroscopy, has been used to improve the identification of cancerous lesions in various organs. Until now, surveys have mainly focused on the nondestructive application of optical sensing used to diagnose and discriminate normal and abnormal biomedical lesions or samples. Meanwhile, the response to the treatment might be monitored using these nondestructive technologies, thereby enabling further therapeutic modification. Methods: The human skin melanoma cell line (A375) donated from Switzerland (University Hospital Basel) was cultured. Vemurafenib (Zelboraf; Genentech/Roche, South San Francisco, CA) was used for cell treatments. The visible-near-infrared reflectance spectroscopy was conducted at different time intervals (before treatment, and at 1, 2, 7, and 14 days post-treatment for three drug doses 5, 25, and 75 μM) on cell plates using the portable CCD-based fiber optical spectrometer (USB2000; Ocean Optics). After data collection, the refractive index analysis for the fore-mentioned doses and days in one selected wavelength of 620 nm was examined using the previously developed computer program. Then, biological assays were selected as gold standard of cell death, apoptosis, and drug resistance gene expression. Results: There was a considerable decrease in the refractive index of cell samples in which biological assay confirmed cell death. Based on the flow cytometry data, a drug dose of 25 μM on day 7 seemed to induce necrosis. These findings show that spectroscopic findings strongly agree with concurrent biological studies and might lead to their use as an alternative method for monitoring treatment response to achieve more optimized cancer treatment. Conclusions: The findings show that reflectance spectroscopy, as a nondestructive real-time label-free way, is capable of providing quantitative information for treatment response determination that corresponds with biological assays.

A priori prediction of response in multicentre locally advanced breast cancer (LABC) patients using quantitative ultrasound and derivative texture methods

PURPOSE

We develop a multi-centric response predictive model using QUS spectral parametric imaging and novel texture-derivate methods for determining tumour responses to neoadjuvant chemotherapy (NAC) prior to therapy initiation.

MATERIALS AND METHODS

QUS Spectroscopy provided parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average-scatterer-diameter (ASD), and average-acoustic-concentration (AAC) in 78 patients with locally advanced breast cancer (LABC) undergoing NAC. Ultrasound radiofrequency data were collected from Sunnybrook Health Sciences Center (SHSC), University of Texas MD Anderson Cancer Center (MD-ACC), and St. Michaels Hospital (SMH) using two different systems. Texture analysis was used to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS, texture- and texture-derivate parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis for developing a response predictive model to classify responders versus non-responders. Model performance was assessed using leave-one-out cross-validation. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest-neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.

RESULTS

A combination of tumour core and margin classification resulted in a peak response prediction performance of 88% sensitivity, 78% specificity, 84% accuracy, 0.86 AUC, 84% PPV, and 83% NPV, achieved using the SVM-RBF classification algorithm. Other parameters and classifiers performed less well running from 66% to 80% accuracy.

CONCLUSIONS

A QUS-based framework and novel texture-derivative method enabled accurate prediction of responses to NAC. Multi-centric response predictive model provides indications of the robustness of the approach to variations due to different ultrasound systems and acquisition parameters.

Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions

PURPOSE

Accurate and timely diagnosis of breast carcinoma is very crucial because of its high incidence and high morbidity. Screening can improve overall prognosis by detecting the disease early. Biopsy remains as the gold standard for pathological confirmation of malignancy and tumour grading. The development of diagnostic imaging techniques as an alternative for the rapid and accurate characterization of breast masses is necessitated. Quantitative ultrasound (QUS) spectroscopy is a modality well suited for this purpose. This study was carried out to evaluate different texture analysis methods applied on QUS spectral parametric images for the characterization of breast lesions.

METHODS

Parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined using QUS spectroscopy from 193 patients with breast lesions. Texture methods were used to quantify heterogeneities of the parametric images. Three statistical-based approaches for texture analysis that include Gray Level Co-occurrence Matrix (GLCM), Gray Level Run-length Matrix (GRLM), and Gray Level Size Zone Matrix (GLSZM) methods were evaluated. QUS and texture-parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis in order to classify breast lesions as either benign or malignant. We developed a diagnostic model using different classification algorithms including linear discriminant analysis (LDA), k-nearest neighbours (KNN), support vector machine with radial basis function kernel (SVM-RBF), and an artificial neural network (ANN). Model performance was evaluated using leave-one-out cross-validation (LOOCV) and hold-out validation.

RESULTS

Classifier performances ranged from 73% to 91% in terms of accuracy dependent on tumour margin inclusion and classifier methodology. Utilizing information from tumour core alone, the ANN achieved the best classification performance of 93% sensitivity, 88% specificity, 91% accuracy, 0.95 AUC using QUS parameters and their GLSZM texture features.

CONCLUSIONS

A QUS-based framework and texture analysis methods enabled classification of breast lesions with >90% accuracy. The results suggest that optimizing method for extracting discriminative textural features from QUS spectral parametric images can improve classification performance. Evaluation of the proposed technique on a larger cohort of patients with proper validation technique demonstrated the robustness and generalization of the approach.

A Brain Tumor Segmentation Framework Based on Outlier Detection Using One-Class Support Vector Machine

Accurate segmentation of brain tumors is a challenging task and also a crucial step in diagnosis and treatment planning for cancer patients. Magnetic resonance imaging (MRI) is the standard imaging modality for detection, characterization, treatment planning and outcome evaluation of brain tumors. MRI scans are usually acquired at multiple sessions before and after the treatment. An automatic segmentation framework is highly desirable to segment brain tumors in MR images as it streamlines the image-guided radiation therapy workflow considerably. Automatic segmentation of brain tumors also facilitates an incremental development of data-driven systems for therapy outcome prediction based on radiomics analysis. In this study, an outlier-detection-based segmentation framework is proposed to delineate brain tumors in magnetic resonance (MR) images automatically. The proposed method considers the tumor and edema pixels in an MR image as outliers compared to the pixels associated with the healthy tissue. The framework generates two outlier masks using independent one-class support vector machines that operate on post-contrast T1-weighted (T1w) and T2-weighted-fluid-attenuation-inversion-recovery (T2-FLAIR) images. The outlier masks are subsequently refined and fused using a number of morphological and logical operators to estimate a tumor mask for each image slice. The framework was constructed and evaluated using the MRI data acquired from 35 and 5 patients with brain metastasis, respectively. The obtained results demonstrated an average Dice similarity coefficient and Hausdorff distance of 0.84 ± 0.06 and 1.85 ± 0.48 mm, respectively, between the manual (ground truth) and automatic tumor contours, on the independent test set.

Predicting Local Failure after Stereotactic Radiation Therapy in Brain Metastasis using Quantitative CT and Machine Learning

Despite recent advances in cancer treatment, the prognosis of patients diagnosed with brain metastasis is still poor. The median survival is limited to months even for patients undergoing treatment. Radiation therapy is a main component of treatment for brain metastasis. However, radiotherapy cannot control local progression in up to 20% of the metastatic brain tumours. An early prediction of radiotherapy outcome for individual patients could facilitate therapy adjustments to improve its efficacy. This study investigated the potential of quantitative CT biomarkers in conjunction with machine learning methods to predict local failure after radiotherapy in brain metastasis. Volumetric CT images were acquired for radiation treatment planning from 120 patients undergoing stereotactic radiotherapy. Quantitative features characterizing the morphology and texture were extracted from different regions of each lesion. A feature reduction/selection framework was adapted to define a quantitative CT biomarker of radiotherapy outcome. Different machine learning methods were applied and evaluated to predict the local failure outcome at pre-treatment. The optimum biomarker consisting of two features in conjunction with an AdaBoost with decision tree could predict the local failure outcome with 71% accuracy on an independent test set (20 patients, 31 lesions). This study is a step forward towards prediction of radiotherapy outcome in brain metastasis using quantitative imaging and machine learning.

Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods

PURPOSE

Accurate and timely diagnosis of breast cancer is extremely important because of its high incidence and high morbidity. Early diagnosis of breast cancer through screening can improve overall prognosis. Currently, biopsy remains as the gold standard for tumor pathological confirmation. Development of diagnostic imaging techniques for rapid and accurate characterization of breast lesions is required. We aim to evaluate the usefulness of texture-derivate features of QUS spectral parametric images for non-invasive characterization of breast lesions.

METHODS

QUS Spectroscopy was used to determine parametric images of mid-band fit (MBF), spectral slope (SS), spectral intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) in 204 patients with suspicious breast lesions. Subsequently, texture analysis techniques were used to generate texture maps from parametric images to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS parameters, texture-parameters and texture-derivate parameters were determined from both tumor core and a 5-mm tumor margin and were used in comparison to histopathological analysis in order to develop a diagnostic model for classifying breast lesions as either benign or malignant. Both leave-one-out and hold-out cross-validations were used to evaluate the performance of the diagnostic model. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.

RESULTS

Core and margin information using the SVM-RBF attained the best classification performance of 90% sensitivity, 92% specificity, 91% accuracy, and 0.93 AUC utilizing QUS parameters and their texture derivatives, evaluated using leave-one-out cross-validation. Implementation of hold-out cross-validation using combination of both core and margin information and SVM-RBF achieved average accuracy and AUC of 88% and 0.92, respectively.

CONCLUSIONS

QUS-based framework and derivative texture methods enable accurate classification of breast lesions. Evaluation of the proposed technique on a large cohort using hold-out cross-validation demonstrates its robustness and its generalization.

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.

Spatial Characterization of Tumor Perfusion Properties from 3D DCE-US Perfusion Maps are Early Predictors of Cancer Treatment Response

There is a need for noninvasive repeatable biomarkers to detect early cancer treatment response and spare non-responders unnecessary morbidities and costs. Here, we introduce three-dimensional (3D) dynamic contrast enhanced ultrasound (DCE-US) perfusion map characterization as inexpensive, bedside and longitudinal indicator of tumor perfusion for prediction of vascular changes and therapy response. More specifically, we developed computational tools to generate perfusion maps in 3D of tumor blood flow, and identified repeatable quantitative features to use in machine-learning models to capture subtle multi-parametric perfusion properties, including heterogeneity. Models were developed and trained in mice data and tested in a separate mouse cohort, as well as early validation clinical data consisting of patients receiving therapy for liver metastases. Models had excellent (ROC-AUC > 0.9) prediction of response in pre-clinical data, as well as proof-of-concept clinical data. Significant correlations with histological assessments of tumor vasculature were noted (Spearman R > 0.70) in pre-clinical data. Our approach can identify responders based on early perfusion changes, using perfusion properties correlated to gold-standard vascular properties.

An Automatic Framework for Segmentation of Brain Tumours at Follow-up Scans after Radiation Therapy

Brain metastasis is the most common intracranial malignancy with a poor overall survival (OS) after treatment. The standard stereotactic radiation therapy (SRT) planning procedure for brain metastasis requires delineating the tumour volume on magnetic resonance (MR) images. MR images are also acquired at multiple follow-up scans after SRT to monitor the treatment outcome through measuring changes in the physical dimensions of the tumour. Such measurements require manual segmentation of the tumour volume on multiple slices of several follow-up images which is tedious and impedes the SRT evaluation work flow considerably. In this study, an automatic framework was proposed to segment the tumour volume on longitudinal MR images acquired at standard follow-up scans after SRT. The multi-step segmentation framework was based on region growing and morphological snakes models that applied the standard SRT planning tumour contour as a basis to approximate the tumour shape and location at each follow-up scan for an accurate automatic segmentation of tumour volume. The framework was evaluated using the MR imaging data acquired from five patients prior to and at three follow-up scans after SRT. The preliminary results indicated that the Dice similarity coefficient between the ground truth tumour masks and their automatically segmented counterparts ranged between 0.84 and 0.90, while the average Dice coefficient for all the follow-up scans was 0.88. The results obtained implied a good potential of the proposed framework for being incorporated into the SRT treatment planning and evaluation systems as well as outcome prediction models.

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-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.

Ultrasound Elastography of the Prostate Using an Unconstrained Modulus Reconstruction Technique: A Pilot Clinical Study

A novel full-inversion-based technique for quantitative ultrasound elastography was investigated in a pilot clinical study on five patients for non-invasive detection and localization of prostate cancer and quantification of its extent. Conventional-frequency ultrasound images and radiofrequency (RF) data (~5 MHz) were collected during mechanical stimulation of the prostate using a transrectal ultrasound probe. Pre and post-compression RF data were used to construct the strain images. The Young's modulus (YM) images were subsequently reconstructed using the derived strain images and the stress distribution estimated iteratively using finite element (FE) analysis. Tumor regions determined based on the reconstructed YM images were compared to whole-mount histopathology images of radical prostatectomy specimens. Results indicated that tumors were significantly stiffer than the surrounding tissue, demonstrating a relative YM of 2.5 ± 0.8 compared to normal prostate tissue. The YM images had a good agreement with the histopathology images in terms of tumor location within the prostate. On average, 76% ± 28% of tumor regions detected based on the proposed method were inside respective tumor areas identified in the histopathology images. Results of a linear regression analysis demonstrated a good correlation between the disease extents estimated using the reconstructed YM images and those determined from whole-mount histopathology images (r² = 0.71). This pilot study demonstrates that the proposed method has a good potential for detection, localization and quantification of prostate cancer. The method can potentially be used for prostate needle biopsy guidance with the aim of decreasing the number of needle biopsies. The proposed technique utilizes conventional ultrasound imaging system only while no additional hardware attachment is required for mechanical stimulation or data acquisition. Therefore, the technique may be regarded as a non-invasive, low cost and potentially widely-available clinical tool for prostate cancer diagnosis.

High frequency ultrasound imaging and simulations of sea urchin oocytes

High frequency ultrasound backscatter signals from sea urchin oocytes were measured using a 40 MHz transducer and compared to numerical simulations. The Faran scattering model was used to calculate the ultrasound scattered from single oocytes in suspension. The urchin oocytes are non-nucleated with uniform size and biomechanical properties; the backscatter from each cell is similar and easy to simulate, unlike typical nucleated mammalian cells. The time domain signal measured from single oocytes in suspension showed two distinct peaks, and the power spectrum was periodic with minima spaced approximately 10 MHz apart. Good agreement to the Faran scattering model was observed. Measurements from tightly packed oocyte cell pellets showed similar periodic features in the power spectra, which was a result of the uniform size and consistent biomechanical properties of the cells. Numerical simulations that calculated the ultrasound scattered from individual oocytes within a three dimensional volume showed good agreement to the measured signals and B-scan images. A cepstral analysis of the signal was used to calculate the size of the cells, which was 78.7 μm (measured) and 81.4 μm (simulated). This work supports the single scattering approximation, where ultrasound is discretely scattered from single cells within a bulk homogeneous sample, and that multiple scattering has a negligible effect. This technique can be applied towards understanding the complex scattering behaviour from heterogeneous tissues.

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.

A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound

Quantitative ultrasound (QUS) can probe tissue structure and analyze tumour characteristics. Using a 6-MHz ultrasound system, radiofrequency data were acquired from 56 locally advanced breast cancer patients prior to their neoadjuvant chemotherapy (NAC) and QUS texture features were computed from regions of interest in tumour cores and their margins as potential predictive and prognostic indicators. Breast tumour molecular features were also collected and used for analysis. A multiparametric QUS model was constructed, which demonstrated a response prediction accuracy of 88% and ability to predict patient 5-year survival rates (p = 0.01). QUS features demonstrated superior performance in comparison to molecular markers and the combination of QUS and molecular markers did not improve response prediction. This study demonstrates, for the first time, that non-invasive QUS features in the core and margin of breast tumours can indicate breast cancer response to neoadjuvant chemotherapy (NAC) and predict five-year recurrence-free survival.

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.

Imaging techniques: new avenues in cancer gene and cell therapy

Cancer is one of the world's most concerning health problems and poses many challenges in the range of approaches associated with the treatment of cancer. Current understanding of this disease brings to the fore a number of novel therapies that can be useful in the treatment of cancer. Among them, gene and cell therapies have emerged as novel and effective approaches. One of the most important challenges for cancer gene and cell therapies is correct monitoring of the modified genes and cells. In fact, visual tracking of therapeutic cells, immune cells, stem cells and genetic vectors that contain therapeutic genes and the various drugs is important in cancer therapy. Similarly, molecular imaging, such as nanosystems, fluorescence, bioluminescence, positron emission tomography, single photon-emission computed tomography and magnetic resonance imaging, have also been found to be powerful tools in monitoring cancer patients who have received therapeutic cell and gene therapies or drug therapies. In this review, we focus on these therapies and their molecular imaging techniques in treating and monitoring the progress of the therapies on various types of cancer.

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.

Intravoxel incoherent motion diffusion-weighted imaging for monitoring chemotherapeutic efficacy in gastric cancer

AIM: To assess intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for monitoring early efficacy of chemotherapy in a human gastric cancer mouse model.

METHODS: IVIM-DWI was performed with 12 b-values (0-800 s/mm²) in 25 human gastric cancer-bearing nude mice at baseline (day 0), and then they were randomly divided into control and 1-, 3-, 5- and 7-d treatment groups (n = 5 per group). The control group underwent longitudinal MRI scans at days 1, 3, 5 and 7, and the treatment groups underwent subsequent MRI scans after a specified 5-fluorouracil/calcium folinate treatment. Together with tumor volumes (TV), the apparent diffusion coefficient (ADC) and IVIM parameters [true water molecular diffusion coefficient (D), perfusion fraction (f) and pseudo-related diffusion coefficient (D^*)] were measured. The differences in those parameters from baseline to each measurement (ΔTV%, ΔADC%, ΔD%, Δf% and ΔD^*%) were calculated. After image acquisition, tumor necrosis, microvessel density (MVD) and cellular apoptosis were evaluated by hematoxylin-eosin (HE), CD31 and terminal-deoxynucleotidyl transferase mediated nick end labeling (TUNEL) staining respectively, to confirm the imaging findings. Mann-Whitney test and Spearman's correlation coefficient analysis were performed.

RESULTS: The observed relative volume increase (ΔTV%) in the treatment group were significantly smaller than those in the control group at day 5 (ΔTV_treatment% = 19.63% ± 3.01% and ΔTV_control% = 83.60% ± 14.87%, P = 0.008) and day 7 (ΔTV_treatment% = 29.07% ± 10.01% and ΔTV_control% = 177.06% ± 63.00%, P = 0.008). The difference in ΔTV% between the treatment and the control groups was not significant at days 1 and 3 after a short duration of treatment. Increases in ADC in the treatment group (ΔADC%_treatment, median, 30.10% ± 18.32%, 36.11% ± 21.82%, 45.22% ± 24.36%) were significantly higher compared with the control group (ΔADC%_control, median, 4.98% ± 3.39%, 6.26% ± 3.08%, 9.24% ± 6.33%) at days 3, 5 and 7 (P = 0.008, P = 0.016, P = 0.008, respectively). Increases in D in the treatment group (ΔD%_treatment, median 17.12% ± 8.20%, 24.16% ± 16.87%, 38.54% ± 19.36%) were higher than those in the control group (ΔD%_control, median -0.13% ± 4.23%, 5.89% ± 4.56%, 5.54% ± 4.44%) at days 1, 3, and 5 (P = 0.032, P = 0.008, P = 0.016, respectively). Relative changes in f were significantly lower in the treatment group compared with the control group at days 1, 3, 5 and 7 follow-up (median, -34.13% ± 16.61% vs 1.68% ± 3.40%, P = 0.016; -50.64% ± 6.82% vs 3.01% ± 6.50%, P = 0.008; -49.93% ± 6.05% vs 0.97% ± 4.38%, P = 0.008, and -46.22% ± 7.75% vs 8.14% ± 6.75%, P = 0.008, respectively). D* in the treatment group decreased significantly compared to those in the control group at all time points (median, -32.10% ± 12.22% vs 1.85% ± 5.54%, P = 0.008; -44.14% ± 14.83% vs 2.29% ± 10.38%, P = 0.008; -59.06% ± 19.10% vs 3.86% ± 5.10%, P = 0.008 and -47.20% ± 20.48% vs 7.13% ± 9.88%, P = 0.016, respectively). Furthermore, histopathologic findings showed positive correlations with ADC and D and tumor necrosis (r_s = 0.720, P < 0.001; r_s = 0.522, P = 0.007, respectively). The cellular apoptosis of the tumor also showed positive correlations with ADC and D (r_s = 0.626, P = 0.001; r_s = 0.542, P = 0.005, respectively). Perfusion-related parameters (f and D^*) were positively correlated to MVD (r_s = 0.618, P = 0.001; r_s = 0.538, P = 0.006, respectively), and negatively correlated to cellular apoptosis of the tumor (r_s = -0.550, P = 0.004; r_s = -0.692, P < 0.001, respectively).

CONCLUSION: IVIM-DWI is potentially useful for predicting the early efficacy of chemotherapy in a human gastric cancer mouse model.

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.

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.

Quantitative evaluation of cell death response in vitro and in vivo using conventional-frequency ultrasound

Previous studies using high-frequency ultrasound have suggested that radiofrequency (RF) spectral analysis can be used to quantify changes in cell morphology to detect cell death response to therapy non-invasively. The study here investigated this at conventional-frequencies, frequently used in clinical settings.

Spectral analysis was performed using ultrasound RF data collected with a clinical ultrasound platform. Acute myeloid leukemia (AML-5) cells were exposed to cisplatinum for 0–72 hours in vitro and prepared for ultrasound data collection. Preclinical in vivo experiments were also performed on AML-5 tumour-bearing mice receiving chemotherapy.

The mid-band fit (MBF) spectral parameter demonstrated an increase of 4.4 ± 1.5 dBr for in vitro samples assessed 48 hours after treatment, a statistically significant change (p < 0.05) compared to control. Further, in vitro concentration-based analysis of a mixture of apoptotic and untreated cells indicated a mean change of 10.9 ± 2.4 dBr in MBF between 0% and 40% apoptotic cell mixtures. Similar effects were reproduced in vivo with an increase of 4.6 ± 0.3 dBr in MBF compared to control, for tumours with considerable apoptotic areas within histological samples. The alterations in the size of cells and nuclei corresponded well with changes measured in the quantitative ultrasound (QUS) parameters.

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.

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.

Low-frequency quantitative ultrasound imaging of cell death in vivo

PURPOSE

Currently, no clinical imaging modality is used routinely to assess tumor response to cancer therapies within hours to days of the delivery of treatment. Here, the authors demonstrate the efficacy of ultrasound at a clinically relevant frequency to quantitatively detect changes in tumors in response to cancer therapies using preclinical mouse models.

METHODS

Conventional low-frequency and corresponding high-frequency ultrasound (ranging from 4 to 28 MHz) were used along with quantitative spectroscopic and signal envelope statistical analyses on data obtained from xenograft tumors treated with chemotherapy, x-ray radiation, as well as a novel vascular targeting microbubble therapy.

RESULTS

Ultrasound-based spectroscopic biomarkers indicated significant changes in cell-death associated parameters in responsive tumors. Specifically changes in the midband fit, spectral slope, and 0-MHz intercept biomarkers were investigated for different types of treatment and demonstrated cell-death related changes. The midband fit and 0-MHz intercept biomarker derived from low-frequency data demonstrated increases ranging approximately from 0 to 6 dBr and 0 to 8 dBr, respectively, depending on treatments administrated. These data paralleled results observed for high-frequency ultrasound data. Statistical analysis of ultrasound signal envelope was performed as an alternative method to obtain histogram-based biomarkers and provided confirmatory results. Histological analysis of tumor specimens indicated up to 61% cell death present in the tumors depending on treatments administered, consistent with quantitative ultrasound findings indicating cell death. Ultrasound-based spectroscopic biomarkers demonstrated a good correlation with histological morphological findings indicative of cell death (r2=0.71, 0.82; p<0.001).

CONCLUSIONS

In summary, the results provide preclinical evidence, for the first time, that quantitative ultrasound used at a clinically relevant frequency, in addition to high-frequency ultrasound, can detect tissue changes associated with cell death in vivo in response to cancer treatments.

Conventional frequency ultrasonic biomarkers of cancer treatment response in vivo

BACKGROUND

Conventional frequency quantitative ultrasound in conjunction with textural analysis techniques was investigated to monitor noninvasively the effects of cancer therapies in an in vivo preclinical model.

METHODS

Conventional low-frequency (∼7 MHz) and high-frequency (∼20 MHz) ultrasound was used with spectral analysis, coupled with textural analysis on spectral parametric maps, obtained from xenograft tumor-bearing animals (n = 20) treated with chemotherapy to extract noninvasive biomarkers of treatment response.

RESULTS

Results indicated statistically significant differences in quantitative ultrasound-based biomarkers in both low- and high-frequency ranges between untreated and treated tumors 12 to 24 hours after treatment. Results of regression analysis indicated a high level of correlation between quantitative ultrasound-based biomarkers and tumor cell death estimates from histologic analysis. Applying textural characterization to the spectral parametric maps resulted in an even stronger correlation (r (2) = 0.97).

CONCLUSION

The results obtained in this research demonstrate that quantitative ultrasound at a clinically relevant frequency can monitor tissue changes in vivo in response to cancer treatment administration. Using higher order textural information extracted from quantitative ultrasound spectral parametric maps provides more information at a high sensitivity related to tumor cell death.

Quantitative ultrasound evaluation of tumor cell death response in locally advanced breast cancer patients receiving chemotherapy

PURPOSE

Quantitative ultrasound techniques have been recently shown to be capable of detecting cell death through studies conducted on in vitro and in vivo models. This study investigates for the first time the potential of early detection of tumor cell death in response to clinical cancer therapy administration in patients using quantitative ultrasound spectroscopic methods.

EXPERIMENTAL DESIGN

Patients (n = 24) with locally advanced breast cancer received neoadjuvant chemotherapy treatments. Ultrasound data were collected before treatment onset and at 4 times during treatment (weeks 1, 4, and 8, and preoperatively). Quantitative ultrasound parameters were evaluated for clinically responsive and nonresponding patients.

RESULTS

Results indicated that quantitative ultrasound parameters showed significant changes for patients who responded to treatment, and no similar alteration was observed in treatment-refractory patients. Such differences between clinically and pathologically determined responding and nonresponding patients were statistically significant (P < 0.05) after 4 weeks of chemotherapy. Responding patients showed changes in parameters related to cell death with, on average, an increase in mid-band fit and 0-MHz intercept of 9.1 ± 1.2 dBr and 8.9 ± 1.9 dBr, respectively, whereas spectral slope was invariant. Linear discriminant analysis revealed a sensitivity of 100% and a specificity of 83.3% for distinguishing nonresponding patients by the fourth week into a course of chemotherapy lasting several months.

CONCLUSION

This study reports for the first time that quantitative ultrasound spectroscopic methods can be applied clinically to evaluate cancer treatment responses noninvasively. The results form a basis for monitoring chemotherapy effects and facilitating the personalization of cancer treatment.

Evaluation of neoadjuvant chemotherapy response in women with locally advanced breast cancer using ultrasound elastography

PURPOSE

Ultrasound elastography is a new imaging technique that can be used to assess tissue stiffness. The aim of this study was to investigate the potential of ultrasound elastography for monitoring treatment response of locally advanced breast cancer patients undergoing neoadjuvant therapy.

METHODS

Fifteen women receiving neoadjuvant chemotherapy had the affected breast scanned before, 1, 4, and 8 weeks following therapy initiation, and then before surgery. Changes in elastographic parameters related to tissue biomechanical properties were then determined and compared to clinical and pathologic tumor response after mastectomy.

RESULTS

Patients who responded to therapy demonstrated a significant decrease (P < .05) in strain ratios and strain differences 4 weeks after treatment initiation compared to non-responding patients. Mean strain ratio and mean strain difference for responders was 81 ± 3% and 1 ± 17% for static regions of interest (ROIs) and 81 ± 3% and 6 ± 18% for dynamic ROIs, respectively. In contrast, these parameters were 102±2%, 110±17%, 101±4%, and 109±30% for non-responding patients, respectively. Strain ratio using static ROIs was found to be the best predictor of treatment response, with 100% sensitivity and 100% specificity obtained 4 weeks after starting treatment.

CONCLUSIONS

These results suggest that ultrasound elastography can be potentially used as an early predictor of tumor therapy response in breast cancer patients.