Quantitative ultrasound imaging of therapy response in bladder cancer in vivo.

Background and aimsQuantitative 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.MethodsTumor-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 conclusionsThere 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

Quantitative Ultrasound Characterization and Monitoring of Locally Advanced Breast Cancer

Traditional assessment of tumour response to cancer therapy is based on tumour size reduction, which takes several weeks to become clinically significant. In this thesis, novel ultrasound backscatter signal analysis and machine learning techniques were developed to characterize breast tumours and detect early changes correlated to response. In the first study, tumour cell death was induced in human breast cancer tumour-bearing mice, using human mimicking chemotherapy drugs. Treatment-related changes in quantitative ultrasound (QUS) parameters, including change in average acoustic concentration (AAC) and heterogeneity index, revealed a strong correlation to histologically determined cell death extent (r2=0.64). In the second study, radiofrequency (RF) ultrasound data were acquired from locally advanced breast cancer (LABC) patients prior to treatment. Results suggested that a multiparameter QUS model can sensitively (88%) and specifically (91%) differentiate breast tumours from surrounding normal tissue. Furthermore, a local texture - based QUS model was demonstrated as a promising tumour grade predictor (86% accuracy). In the final study, ultrasound RF data were acquired from LABC patients prior to treatment, at 3 times during the treatment (weeks 1, 4, 8), and prior to surgery. Tumour response classification analysis using a multiparameter QUS model of midband fit (MBF), spectral slope (SS), and spacing among scatterers (SAS) demonstrated desirable classification performance at 4 weeks into treatment (80 ± 5%). Secondly, the QUS classification model demonstrated a significant difference in survival rates of responding and nonresponding patients at weeks 1 and 4 (p=0.035, and 0.027, respectively). In summary, the incorporation of QUS assessment of the breast during or after an ultrasound-guided breast biopsy session may potentially permit cross-verification of the histopathological findings. Furthermore, patients undergoing neoadjuvant chemotherapy can potentially benefit from a weekly QUS assessment in order to evaluate their early tumour response so that the appropriate treatment intervention can be made if the patient was nonresponding.Ph.D

Noninvasive Characterization of Locally Advanced Breast Cancer Using Textural Analysis of Quantitative Ultrasound Parametric Images

PURPOSE: The identification of tumor pathologic characteristics is an important part of breast cancer diagnosis, prognosis, and treatment planning but currently requires biopsy as its standard. Here, we investigated a noninvasive quantitative ultrasound method for the characterization of breast tumors in terms of their histologic grade, which can be used with clinical diagnostic ultrasound data. METHODS: Tumors of 57 locally advanced breast cancer patients were analyzed as part of this study. Seven quantitative ultrasound parameters were determined from each tumor region from the radiofrequency data, including mid-band fit, spectral slope, 0-MHz intercept, scatterer spacing, attenuation coefficient estimate, average scatterer diameter, and average acoustic concentration. Parametric maps were generated corresponding to the region of interest, from which four textural features, including contrast, energy, homogeneity, and correlation, were determined as further tumor characterization parameters. Data were examined on the basis of tumor subtypes based on histologic grade (grade I versus grade II to III). RESULTS: Linear discriminant analysis of the means of the parametric maps resulted in classification accuracy of 79%. On the other hand, the linear combination of the texture features of the parametric maps resulted in classification accuracy of 82%. Finally, when both the means and textures of the parametric maps were combined, the best classification accuracy was obtained (86%). CONCLUSIONS: Textural characteristics of quantitative ultrasound spectral parametric maps provided discriminant information about different types of breast tumors. The use of texture features significantly improved the results of ultrasonic tumor characterization compared to conventional mean values. Thus, this study suggests that texture-based quantitative ultrasound analysis of in vivo breast tumors can provide complementary diagnostic information about tumor histologic characteristics

Prostate cancer: Relationship between vascular diameter, shape and density and Gleason score in needle biopsy specimens

Background: Tumor growth requires expansion and development of vascular network. An increase in Gleason score is representative of an increase in tumor invasion and extent. In this study, the relationship between Gleason score and vascular characteristics of needle biopsy samples in prostate cancer patients has been evaluated. Materials and Methods: We evaluated vascular characteristics including density and size of vessels; and percentage of vessels with irregular shape in 62 cancer-positive samples obtained by prostate needle biopsy under ultrasound guide, and compared them to Gleason score. Result: Gleason scores of 23 patients were ≤6; Gleason scores of 18 patients were 7 and 21 patients had their Gleason score from 8 to 10. An increase in Gleason score was associated with increased vascular density (P < 0.0001), increased percentage of vessels with irregular shape (P < 0.02) and decreased average vascular diameter (P < 0.015), from which the relationship with vascular density was clearer and more evident. Conclusion: Vascular morphological characteristics can be representative of angiogenic potential of prostate cancer which is required for tumor progression. As Gleason score can prognosticate the behavioral characteristics of prostate cancer in future, vascular characteristics may also be able to express tumor behavior. With attention to vascular characteristics in biopsy samples and apart from Gleason score, we may also be able to divide patients into other subtypes in a way being helpful for the establishment of treatment plan

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

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

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

<div><p>Background</p><p>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.</p><p>Methods</p><p>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.</p><p>Results</p><p>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.</p><p>Conclusions</p><p>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.</p></div