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Falou O, Sannachi L, Haque M, Czarnota GJ, Kolios MC. Transfer learning of pre-treatment quantitative ultrasound multi-parametric images for the prediction of breast cancer response to neoadjuvant chemotherapy. Sci Rep 2024; 14:2340. [PMID: 38282158 PMCID: PMC10822849 DOI: 10.1038/s41598-024-52858-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 01/24/2024] [Indexed: 01/30/2024] Open
Abstract
Locally advanced breast cancer (LABC) is a severe type of cancer with a poor prognosis, despite advancements in therapy. As the disease is often inoperable, current guidelines suggest upfront aggressive neoadjuvant chemotherapy (NAC). Complete pathological response to chemotherapy is linked to improved survival, but conventional clinical assessments like physical exams, mammography, and imaging are limited in detecting early response. Early detection of tissue response can improve complete pathological response and patient survival while reducing exposure to ineffective and potentially harmful treatments. A rapid, cost-effective modality without the need for exogenous contrast agents would be valuable for evaluating neoadjuvant therapy response. Conventional ultrasound provides information about tissue echogenicity, but image comparisons are difficult due to instrument-dependent settings and imaging parameters. Quantitative ultrasound (QUS) overcomes this by using normalized power spectra to calculate quantitative metrics. This study used a novel transfer learning-based approach to predict LABC response to neoadjuvant chemotherapy using QUS imaging at pre-treatment. Using data from 174 patients, QUS parametric images of breast tumors with margins were generated. The ground truth response to therapy for each patient was based on standard clinical and pathological criteria. The Residual Network (ResNet) deep learning architecture was used to extract features from the parametric QUS maps. This was followed by SelectKBest and Synthetic Minority Oversampling (SMOTE) techniques for feature selection and data balancing, respectively. The Support Vector Machine (SVM) algorithm was employed to classify patients into two distinct categories: nonresponders (NR) and responders (RR). Evaluation results on an unseen test set demonstrate that the transfer learning-based approach using spectral slope parametric maps had the best performance in the identification of nonresponders with precision, recall, F1-score, and balanced accuracy of 100, 71, 83, and 86%, respectively. The transfer learning-based approach has many advantages over conventional deep learning methods since it reduces the need for large image datasets for training and shortens the training time. The results of this study demonstrate the potential of transfer learning in predicting LABC response to neoadjuvant chemotherapy before the start of treatment using quantitative ultrasound imaging. Prediction of NAC response before treatment can aid clinicians in customizing ineffectual treatment regimens for individual patients.
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Affiliation(s)
- Omar Falou
- Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada.
- Institute for Biomedical Engineering, Science and Technology (iBEST), Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada.
| | - Lakshmanan Sannachi
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Maeashah Haque
- Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada
| | - Gregory J Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Michael C Kolios
- Department of Physics, Toronto Metropolitan University, Toronto, ON, Canada
- Institute for Biomedical Engineering, Science and Technology (iBEST), Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
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Sharma D, Sannachi L, Osapoetra LO, Cartar H, Cui W, Giles A, Czarnota GJ. Noninvasive Evaluation of Breast Tumor Response to Combined Ultrasound-Stimulated Microbubbles and Hyperthermia Therapy Using Quantitative Ultrasound-Based Texture Analysis Method. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2024; 43:137-150. [PMID: 37873733 DOI: 10.1002/jum.16347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 09/19/2023] [Accepted: 09/23/2023] [Indexed: 10/25/2023]
Abstract
OBJECTIVES Quantitative ultrasound (QUS) is a noninvasive imaging technique that can be used for assessing response to anticancer treatment. In the present study, tumor cell death response to the ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) treatment was monitored in vivo using QUS. METHODS Human breast cancer cell lines (MDA-MB-231) were grown in mice and were treated with HT (10, 30, 50, and 60 minutes) alone, or in combination with USMB. Treatment effects were examined using QUS with a center frequency of 25 MHz (bandwidth range: 16 to 32 MHz). Backscattered radiofrequency (RF) data were acquired from tumors subjected to treatment. Ultrasound parameters such as average acoustic concentration (AAC) and average scatterer diameter (ASD), were estimated 24 hours prior and posttreatment. Additionally, texture features: contrast (CON), correlation (COR), energy (ENE), and homogeneity (HOM) were extracted from QUS parametric maps. All estimated parameters were compared with histopathological findings. RESULTS The findings of our study demonstrated a significant increase in QUS parameters in both treatment conditions: HT alone (starting from 30 minutes of heat exposure) and combined treatment of HT plus USMB finally reaching a maximum at 50 minutes of heat exposure. Increase in AAC for 50 minutes HT alone and USMB +50 minutes was found to be 5.19 ± 0.417% and 5.91 ± 1.11%, respectively, compared to the control group with AAC value of 1.00 ± 0.44%. Furthermore, between the treatment groups, ΔASD-ENE values for USMB +30 minutes HT significantly reduced, depicting 0.00062 ± 0.00096% compared to 30 minutes HT only group, showing 0.0058 ± 0.0013%. Further, results obtained from the histological analysis indicated greater cell death and reduced nucleus size in both HT alone and HT combined with USMB. CONCLUSION The texture-based QUS parameters indicated a correlation with microstructural changes obtained from histological data. This work demonstrated the use of QUS to detect HT treatment effects in breast cancer tumors in vivo.
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Affiliation(s)
- Deepa Sharma
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Departments of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Lakshmanan Sannachi
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Departments of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Laurentius Oscar Osapoetra
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Departments of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Holliday Cartar
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Wentao Cui
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Anoja Giles
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Gregory J Czarnota
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Departments of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
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Kheirkhah N, Kornecki A, Czarnota GJ, Samani A, Sadeghi-Naini A. Enhanced full-inversion-based ultrasound elastography for evaluating tumor response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. Phys Med 2023; 112:102619. [PMID: 37343438 DOI: 10.1016/j.ejmp.2023.102619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/15/2023] [Accepted: 06/05/2023] [Indexed: 06/23/2023] Open
Abstract
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.
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Affiliation(s)
- Niusha Kheirkhah
- School of Biomedical Engineering, Western University, London, ON, Canada
| | - Anat Kornecki
- Department of Medical Imaging, Western University, London, ON, Canada
| | - Gregory J Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Abbas Samani
- School of Biomedical Engineering, Western University, London, ON, Canada; Departments of Medical Biophysics, Western University, London, ON, Canada; Department of Electrical and Computer Engineering, Western University, London, ON, Canada; Imaging Research, Robarts Research Institute, Western University, London, ON, Canada
| | - Ali Sadeghi-Naini
- School of Biomedical Engineering, Western University, London, ON, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada; Department of Electrical Engineering and Computer Science, York University, Toronto, ON, Canada.
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Singla R, Hu R, Ringstrom C, Lessoway V, Reid J, Nguan C, Rohling R. The Kidneys Are Not All Normal: Transplanted Kidneys and Their Speckle Distributions. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:1268-1274. [PMID: 36842904 DOI: 10.1016/j.ultrasmedbio.2023.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/21/2022] [Accepted: 01/19/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVE Modelling ultrasound speckle to characterise tissue properties has generated considerable interest. As speckle is dependent on the underlying tissue architecture, modelling it may aid in tasks such as segmentation or disease detection. For the transplanted kidney, where ultrasound is used to investigate dysfunction, it is unknown which statistical distribution best characterises such speckle. This applies to the regions of the transplanted kidney: the cortex, the medulla and the central echogenic complex. Furthermore, it is unclear how these distributions vary by patient variables such as age, sex, body mass index, primary disease or donor type. These traits may influence speckle modelling given their influence on kidney anatomy. We investigate these two aims. METHODS B-mode images from n = 821 kidney transplant recipients (one image per recipient) were automatically segmented into the cortex, medulla and central echogenic complex using a neural network. Seven distinct probability distributions were fitted to each region's histogram, and statistical analysis was performed. DISCUSSION The Rayleigh and Nakagami distributions had model parameters that differed significantly between the three regions (p ≤ 0.05). Although both had excellent goodness of fit, the Nakagami had higher Kullbeck-Leibler divergence. Recipient age correlated weakly with scale in the cortex (Ω: ρ = 0.11, p = 0.004), while body mass index correlated weakly with shape in the medulla (m: ρ = 0.08, p = 0.04). Neither sex, primary disease nor donor type exhibited any correlation. CONCLUSION We propose the Nakagami distribution be used to characterize transplanted kidneys regionally independent of disease etiology and most patient characteristics.
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Affiliation(s)
- Rohit Singla
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Ricky Hu
- Faculty of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Cailin Ringstrom
- Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Victoria Lessoway
- Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Janice Reid
- Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Christopher Nguan
- Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Robert Rohling
- Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada; Mechanical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
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Khairalseed M, Hoyt K. High-Resolution Ultrasound Characterization of Local Scattering in Cancer Tissue. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:951-960. [PMID: 36681609 PMCID: PMC9974749 DOI: 10.1016/j.ultrasmedbio.2022.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 11/21/2022] [Accepted: 11/23/2022] [Indexed: 06/17/2023]
Abstract
Ultrasound (US) has afforded an approach to tissue characterization for more than a decade. The challenge is to reveal hidden patterns in the US data that describe tissue function and pathology that cannot be seen in conventional US images. Our group has developed a high-resolution analysis technique for tissue characterization termed H-scan US, an imaging method used to interpret the relative size of acoustic scatterers. In the present study, the objective was to compare local H-scan US image intensity with registered histologic measurements made directly at the cellular level. Human breast cancer cells (MDA-MB 231, American Type Culture Collection, Manassas, VA, USA) were orthotopically implanted into female mice (N = 5). Tumors were allowed to grow for approximately 4 wk before the study started. In vivo imaging of tumor tissue was performed using a US system (Vantage 256, Verasonics Inc., Kirkland, WA, USA) equipped with a broadband capacitive micromachined ultrasonic linear array transducer (Kolo Medical, San Jose, CA, USA). A 15-MHz center frequency was used for plane wave imaging with five angles for spatial compounding. H-scan US image reconstruction involved use of parallel convolution filters to measure the relative strength of backscattered US signals. Color codes were applied to filter outputs to form the final H-scan US image display. For histologic processing, US imaging cross-sections were carefully marked on the tumor surface, and tumors were excised and sliced along the same plane. By use of optical microscopy, whole tumor tissue sections were scanned and digitized after nuclear staining. US images were interpolated to have the same number of pixels as the histology images and then spatially aligned. Each nucleus from the histologic sections was automatically segmented using custom MATLAB software (The MathWorks Inc., Natick, MA, USA). Nuclear size and spacing from the histology images were then compared with local H-scan US image features. Overall, local H-scan US image intensity exhibited a significant correlation with both cancer cell nuclear size (R2 > 0.27, p < 0.001) and the inverse relationship with nuclear spacing (R2 > 0.17, p < 0.001).
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Affiliation(s)
- Mawia Khairalseed
- Department of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA
| | - Kenneth Hoyt
- Department of Bioengineering, University of Texas at Dallas, Richardson, Texas, USA.
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Hoerig C, Wallace K, Wu M, Mamou J. Classification of Metastatic Lymph Nodes In Vivo Using Quantitative Ultrasound at Clinical Frequencies. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:787-801. [PMID: 36470739 DOI: 10.1016/j.ultrasmedbio.2022.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/28/2022] [Accepted: 10/30/2022] [Indexed: 06/17/2023]
Abstract
Quantitative ultrasound (QUS) methods characterizing the backscattered echo signal have been of use in assessing tissue microstructure. High-frequency (30 MHz) QUS methods have been successful in detecting metastases in surgically excised lymph nodes (LNs), but limited evidence exists regarding the efficacy of QUS for evaluating LNs in vivo at clinical frequencies (2-10 MHz). In this study, a clinical scanner and 10-MHz linear probe were used to collect radiofrequency (RF) echo data of LNs in vivo from 19 cancer patients. QUS methods were applied to estimate parameters derived from the backscatter coefficient (BSC) and statistics of the envelope-detected RF signal. QUS parameters were used to train classifiers based on linear discriminant analysis (LDA) and support vector machines (SVMs). Two BSC-based parameters, scatterer diameter and acoustic concentration, were the most effective for accurately detecting metastatic LNs, with both LDA and SVMs achieving areas under the receiver operating characteristic (AUROC) curve ≥0.94. A strategy of classifying LNs based on the echo frame with the highest cancer probability improved performance to 88% specificity at 100% sensitivity (AUROC = 0.99). These results provide encouraging evidence that QUS applied at clinical frequencies may be effective at accurately identifying metastatic LNs in vivo, helping in diagnosis while reducing unnecessary biopsies and surgical treatments.
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Affiliation(s)
- Cameron Hoerig
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.
| | | | - Maoxin Wu
- Department of Pathology, Stony Brook University, Stony Brook, New York, USA
| | - Jonathan Mamou
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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Sebastian JA, Strohm EM, Baranger J, Villemain O, Kolios MC, Simmons CA. Assessing engineered tissues and biomaterials using ultrasound imaging: In vitro and in vivo applications. Biomaterials 2023; 296:122054. [PMID: 36842239 DOI: 10.1016/j.biomaterials.2023.122054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 01/24/2023] [Accepted: 02/11/2023] [Indexed: 02/18/2023]
Abstract
Quantitative assessment of the structural, functional, and mechanical properties of engineered tissues and biomaterials is fundamental to their development for regenerative medicine applications. Ultrasound (US) imaging is a non-invasive, non-destructive, and cost-effective technique capable of longitudinal and quantitative monitoring of tissue structure and function across centimeter to sub-micron length scales. Here we present the fundamentals of US to contextualize its application for the assessment of biomaterials and engineered tissues, both in vivo and in vitro. We review key studies that demonstrate the versatility and broad capabilities of US for clinical and pre-clinical biomaterials research. Finally, we highlight emerging techniques that further extend the applications of US, including for ultrafast imaging of biomaterials and engineered tissues in vivo and functional monitoring of stem cells, organoids, and organ-on-a-chip systems in vitro.
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Affiliation(s)
- Joseph A Sebastian
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada; Translational Biology and Engineering Program, Ted Rogers Center for Heart Research, Toronto, Canada.
| | - Eric M Strohm
- Translational Biology and Engineering Program, Ted Rogers Center for Heart Research, Toronto, Canada; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
| | - Jérôme Baranger
- Labatt Family Heart Centre, The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Olivier Villemain
- Labatt Family Heart Centre, The Hospital for Sick Children, University of Toronto, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Michael C Kolios
- Department of Physics, Toronto Metropolitan University, Toronto, Canada; Institute of Biomedical Engineering, Science and Technology (iBEST), A Partnership Between Toronto Metropolitan University and St. Michael's Hospital, Toronto, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | - Craig A Simmons
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada; Translational Biology and Engineering Program, Ted Rogers Center for Heart Research, Toronto, Canada; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada.
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Wiacek A, Oluyemi E, Myers K, Ambinder E, Bell MAL. Coherence Metrics for Reader-Independent Differentiation of Cystic From Solid Breast Masses in Ultrasound Images. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:256-268. [PMID: 36333154 PMCID: PMC9712258 DOI: 10.1016/j.ultrasmedbio.2022.08.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 08/22/2022] [Accepted: 08/28/2022] [Indexed: 06/16/2023]
Abstract
Traditional breast ultrasound imaging is a low-cost, real-time and portable method to assist with breast cancer screening and diagnosis, with particular benefits for patients with dense breast tissue. We previously demonstrated that incorporating coherence-based beamforming additionally improves the distinction of fluid-filled from solid breast masses, based on qualitative image interpretation by board-certified radiologists. However, variable sensitivity (range: 0.71-1.00 when detecting fluid-filled masses) was achieved by the individual radiologist readers. Therefore, we propose two objective coherence metrics, lag-one coherence (LOC) and coherence length (CL), to quantitatively determine the content of breast masses without requiring reader assessment. Data acquired from 31 breast masses were analyzed. Ideal separation (i.e., 1.00 sensitivity and specificity) was achieved between fluid-filled and solid breast masses based on the mean or median LOC value within each mass. When separated based on mean and median CL values, the sensitivity/specificity decreased to 1.00/0.95 and 0.92/0.89, respectively. The greatest sensitivity and specificity were achieved in dense, rather than non-dense, breast tissue. These results support the introduction of an objective, reader-independent method for automated diagnoses of cystic breast masses.
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Affiliation(s)
- Alycen Wiacek
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
| | - Eniola Oluyemi
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Kelly Myers
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Emily Ambinder
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Muyinatu A Lediju Bell
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA; Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA
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Implementation of Non-Invasive Quantitative Ultrasound in Clinical Cancer Imaging. Cancers (Basel) 2022; 14:cancers14246217. [PMID: 36551702 PMCID: PMC9776858 DOI: 10.3390/cancers14246217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022] Open
Abstract
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.
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Lye TH, Gachouch O, Renner L, Elezkurtaj S, Cash H, Messroghli D, Raum K, Mamou J. Quantitative Ultrasound Assessment of Early Osteoarthritis in Human Articular Cartilage Using a High-Frequency Linear Array Transducer. ULTRASOUND IN MEDICINE & BIOLOGY 2022; 48:1429-1440. [PMID: 35537895 PMCID: PMC9246887 DOI: 10.1016/j.ultrasmedbio.2022.03.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 06/01/2023]
Abstract
Quantitative ultrasound (QUS) assessment of osteoarthritis (OA) using high-frequency, research-grade single-element ultrasound systems has been reported. The objective of this ex vivo study was to assess the performance of QUS in detecting early OA using a high-frequency linear array transducer. Osteochondral plugs (n = 26) of human articular cartilage were scanned with ExactVu Micro-Ultrasound using an EV29L side-fire transducer. For comparison, the samples were also imaged with SAM200Ex, a custom 40-MHz scanning acoustic microscope with a single-element, focused transducer. Thirteen QUS parameters were derived from the ultrasound data. Magnetic resonance imaging (MRI) data, with T1 and T2 extracted as the quantitative parameters, were also acquired for comparison. Cartilage degeneration was graded from histology and correlated to all quantitative parameters. A maximum Spearman rank correlation coefficient (ρ) of 0.75 was achieved using a combination of ExactVu QUS parameters, while a maximum ρ of 0.62 was achieved using a combination of parameters from SAM200Ex. A maximum ρ of 0.75 was achieved using the T1 and T2 MRI parameters. This study illustrates the potential of a high-frequency linear array transducer to provide a convenient method for early OA screening with results comparable to those of research-grade single-element ultrasound and MRI.
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Affiliation(s)
- Theresa H Lye
- Frederic L. Lizzi Center for Biomedical Engineering, Riverside Research, New York, New York, USA
| | - Omar Gachouch
- Berlin Institute of Health, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Lisa Renner
- Centrum für Muskuloskeletale Chirurgie (CMSC), Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Sefer Elezkurtaj
- Institut für Pathologie, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Hannes Cash
- Department of Urology, Otto-von-Guericke-University Magdeburg, Germany and PROURO, Berlin, Germany
| | - Daniel Messroghli
- Department of Internal Medicine and Cardiology, Deutsches Herzzentrum Berlin and Charite-Universitätsmedizin Berlin, Berlin, Germany
| | - Kay Raum
- Berlin Institute of Health, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jonathan Mamou
- Frederic L. Lizzi Center for Biomedical Engineering, Riverside Research, New York, New York, USA.
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Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies. Sci Rep 2022; 12:9690. [PMID: 35690630 PMCID: PMC9188550 DOI: 10.1038/s41598-022-13917-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 05/30/2022] [Indexed: 11/17/2022] Open
Abstract
Complete pathological response (pCR) to neoadjuvant chemotherapy (NAC) is a prognostic factor for breast cancer (BC) patients and is correlated with improved survival. However, pCR rates are variable to standard NAC, depending on BC subtype. This study investigates quantitative digital histopathology coupled with machine learning (ML) to predict NAC response a priori. Clinicopathologic data and digitized slides of BC core needle biopsies were collected from 149 patients treated with NAC. The nuclei within the tumor regions were segmented on the histology images of biopsy samples using a weighted U-Net model. Five pathomic feature subsets were extracted from segmented digitized samples, including the morphological, intensity-based, texture, graph-based and wavelet features. Seven ML experiments were conducted with different feature sets to develop a prediction model of therapy response using a gradient boosting machine with decision trees. The models were trained and optimized using a five-fold cross validation on the training data and evaluated using an unseen independent test set. The prediction model developed with the best clinical features (tumor size, tumor grade, age, and ER, PR, HER2 status) demonstrated an area under the ROC curve (AUC) of 0.73. Various pathomic feature subsets resulted in models with AUCs in the range of 0.67 and 0.87, with the best results associated with the graph-based and wavelet features. The selected features among all subsets of the pathomic and clinicopathologic features included four wavelet and three graph-based features and no clinical features. The predictive model developed with these features outperformed the other models, with an AUC of 0.90, a sensitivity of 85% and a specificity of 82% on the independent test set. The results demonstrated the potential of quantitative digital histopathology features integrated with ML methods in predicting BC response to NAC. This study is a step forward towards precision oncology for BC patients to potentially guide future therapies.
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Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy. Sci Rep 2022; 12:2244. [PMID: 35145158 PMCID: PMC8831592 DOI: 10.1038/s41598-022-06100-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 01/20/2022] [Indexed: 12/24/2022] Open
Abstract
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.
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Park J, Lee JM, Lee G, Jeon SK, Joo I. Quantitative Evaluation of Hepatic Steatosis Using Advanced Imaging Techniques: Focusing on New Quantitative Ultrasound Techniques. Korean J Radiol 2022; 23:13-29. [PMID: 34983091 PMCID: PMC8743150 DOI: 10.3348/kjr.2021.0112] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 07/26/2021] [Accepted: 08/31/2021] [Indexed: 12/12/2022] Open
Abstract
Nonalcoholic fatty liver disease, characterized by excessive accumulation of fat in the liver, is the most common chronic liver disease worldwide. The current standard for the detection of hepatic steatosis is liver biopsy; however, it is limited by invasiveness and sampling errors. Accordingly, MR spectroscopy and proton density fat fraction obtained with MRI have been accepted as non-invasive modalities for quantifying hepatic steatosis. Recently, various quantitative ultrasonography techniques have been developed and validated for the quantification of hepatic steatosis. These techniques measure various acoustic parameters, including attenuation coefficient, backscatter coefficient and speckle statistics, speed of sound, and shear wave elastography metrics. In this article, we introduce several representative quantitative ultrasonography techniques and their diagnostic value for the detection of hepatic steatosis.
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Affiliation(s)
- Junghoan Park
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jeong Min Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
| | - Gunwoo Lee
- Ultrasound R&D 2 Group, Health & Medical Equipment Business, Samsung Electronics Co., Ltd., Seoul, Korea
| | - Sun Kyung Jeon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
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Wang J, Chu Y, Wang B, Jiang T. A Narrative Review of Ultrasound Technologies for the Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer. Cancer Manag Res 2021; 13:7885-7895. [PMID: 34703310 PMCID: PMC8523361 DOI: 10.2147/cmar.s331665] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/29/2021] [Indexed: 12/21/2022] Open
Abstract
The incidence and mortality rate of breast cancer (BC) in women currently ranks first worldwide, and neoadjuvant chemotherapy (NAC) is widely used in patients with BC. A variety of imaging assessment methods have been used to predict and evaluate the response to NAC. Ultrasound (US) has many advantages, such as being inexpensive and offering a convenient modality for follow-up detection without radiation emission. Although conventional grayscale US is typically used to predict the response to NAC, this approach is limited in its ability to distinguish viable tumor tissue from fibrotic scar tissue. Contrast-enhanced ultrasound (CEUS) combined with a time-intensity curve (TIC) not only provides information on blood perfusion but also reveals a variety of quantitative parameters; elastography has the potential capacity to predict NAC efficiency by evaluating tissue stiffness. Both CEUS and elastography can greatly improve the accuracy of predicting NAC responses. Other US techniques, including three-dimensional (3D) techniques, quantitative ultrasound (QUS) and US-guided near-infrared (NIR) diffuse optical tomography (DOT) systems, also have advantages in assessing NAC response. This paper reviews the different US technologies used for predicting NAC response in BC patients based on the previous literature.
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Affiliation(s)
- Jing Wang
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China
| | - Yanhua Chu
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China
| | - Baohua Wang
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China
| | - Tianan Jiang
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People's Republic of China
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15
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Venkatasamy A, Guerin E, Blanchet A, Orvain C, Devignot V, Jung M, Jung AC, Chenard MP, Romain B, Gaiddon C, Mellitzer G. Ultrasound and Transcriptomics Identify a Differential Impact of Cisplatin and Histone Deacetylation on Tumor Structure and Microenvironment in a Patient-Derived In Vivo Model of Gastric Cancer. Pharmaceutics 2021; 13:pharmaceutics13091485. [PMID: 34575561 PMCID: PMC8467189 DOI: 10.3390/pharmaceutics13091485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 01/06/2023] Open
Abstract
The reasons behind the poor efficacy of transition metal-based chemotherapies (e.g., cisplatin) or targeted therapies (e.g., histone deacetylase inhibitors, HDACi) on gastric cancer (GC) remain elusive and recent studies suggested that the tumor microenvironment could contribute to the resistance. Hence, our objective was to gain information on the impact of cisplatin and the pan-HDACi SAHA (suberanilohydroxamic acid) on the tumor substructure and microenvironment of GC, by establishing patient-derived xenografts of GC and a combination of ultrasound, immunohistochemistry, and transcriptomics to analyze. The tumors responded partially to SAHA and cisplatin. An ultrasound gave more accurate tumor measures than a caliper. Importantly, an ultrasound allowed a noninvasive real-time access to the tumor substructure, showing differences between cisplatin and SAHA. These differences were confirmed by immunohistochemistry and transcriptomic analyses of the tumor microenvironment, identifying specific cell type signatures and transcription factor activation. For instance, cisplatin induced an "epithelial cell like" signature while SAHA favored a "mesenchymal cell like" one. Altogether, an ultrasound allowed a precise follow-up of the tumor progression while enabling a noninvasive real-time access to the tumor substructure. Combined with transcriptomics, our results underline the different intra-tumoral structural changes caused by both drugs that impact differently on the tumor microenvironment.
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Affiliation(s)
- Aina Venkatasamy
- Streinth Lab (Stress Response and Innovative Therapies), Strasbourg University, Inserm UMR_S 1113 IRFAC (Interface Recherche Fondamental et Appliquée à la Cancérologie), 67200 Strasbourg, France; (A.V.); (E.G.); (A.B.); (C.O.); (V.D.); (A.C.J.); (B.R.)
- IHU-Strasbourg (Institut Hospitalo-Universitaire), 67091 Strasbourg, France
| | - Eric Guerin
- Streinth Lab (Stress Response and Innovative Therapies), Strasbourg University, Inserm UMR_S 1113 IRFAC (Interface Recherche Fondamental et Appliquée à la Cancérologie), 67200 Strasbourg, France; (A.V.); (E.G.); (A.B.); (C.O.); (V.D.); (A.C.J.); (B.R.)
| | - Anais Blanchet
- Streinth Lab (Stress Response and Innovative Therapies), Strasbourg University, Inserm UMR_S 1113 IRFAC (Interface Recherche Fondamental et Appliquée à la Cancérologie), 67200 Strasbourg, France; (A.V.); (E.G.); (A.B.); (C.O.); (V.D.); (A.C.J.); (B.R.)
| | - Christophe Orvain
- Streinth Lab (Stress Response and Innovative Therapies), Strasbourg University, Inserm UMR_S 1113 IRFAC (Interface Recherche Fondamental et Appliquée à la Cancérologie), 67200 Strasbourg, France; (A.V.); (E.G.); (A.B.); (C.O.); (V.D.); (A.C.J.); (B.R.)
| | - Véronique Devignot
- Streinth Lab (Stress Response and Innovative Therapies), Strasbourg University, Inserm UMR_S 1113 IRFAC (Interface Recherche Fondamental et Appliquée à la Cancérologie), 67200 Strasbourg, France; (A.V.); (E.G.); (A.B.); (C.O.); (V.D.); (A.C.J.); (B.R.)
| | | | - Alain C. Jung
- Streinth Lab (Stress Response and Innovative Therapies), Strasbourg University, Inserm UMR_S 1113 IRFAC (Interface Recherche Fondamental et Appliquée à la Cancérologie), 67200 Strasbourg, France; (A.V.); (E.G.); (A.B.); (C.O.); (V.D.); (A.C.J.); (B.R.)
- Laboratoire de Biologie Tumorale, ICANS, 67200 Strasbourg, France
| | - Marie-Pierre Chenard
- Pathology Department, Hôpitaux Universitaires de Strasbourg, 67000 Strasbourg, France;
| | - Benoit Romain
- Streinth Lab (Stress Response and Innovative Therapies), Strasbourg University, Inserm UMR_S 1113 IRFAC (Interface Recherche Fondamental et Appliquée à la Cancérologie), 67200 Strasbourg, France; (A.V.); (E.G.); (A.B.); (C.O.); (V.D.); (A.C.J.); (B.R.)
- Digestive Surgery Department, Hôpitaux Universitaires de Strasbourg, 67000 Strasbourg, France
| | - Christian Gaiddon
- Streinth Lab (Stress Response and Innovative Therapies), Strasbourg University, Inserm UMR_S 1113 IRFAC (Interface Recherche Fondamental et Appliquée à la Cancérologie), 67200 Strasbourg, France; (A.V.); (E.G.); (A.B.); (C.O.); (V.D.); (A.C.J.); (B.R.)
- Correspondence: (C.G.); (G.M.)
| | - Georg Mellitzer
- Streinth Lab (Stress Response and Innovative Therapies), Strasbourg University, Inserm UMR_S 1113 IRFAC (Interface Recherche Fondamental et Appliquée à la Cancérologie), 67200 Strasbourg, France; (A.V.); (E.G.); (A.B.); (C.O.); (V.D.); (A.C.J.); (B.R.)
- Correspondence: (C.G.); (G.M.)
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Cloutier G, Destrempes F, Yu F, Tang A. Quantitative ultrasound imaging of soft biological tissues: a primer for radiologists and medical physicists. Insights Imaging 2021; 12:127. [PMID: 34499249 PMCID: PMC8429541 DOI: 10.1186/s13244-021-01071-w] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 08/07/2021] [Indexed: 12/26/2022] Open
Abstract
Quantitative ultrasound (QUS) aims at quantifying interactions between ultrasound and biological tissues. QUS techniques extract fundamental physical properties of tissues based on interactions between ultrasound waves and tissue microstructure. These techniques provide quantitative information on sub-resolution properties that are not visible on grayscale (B-mode) imaging. Quantitative data may be represented either as a global measurement or as parametric maps overlaid on B-mode images. Recently, major ultrasound manufacturers have released speed of sound, attenuation, and backscatter packages for tissue characterization and imaging. Established and emerging clinical applications are currently limited and include liver fibrosis staging, liver steatosis grading, and breast cancer characterization. On the other hand, most biological tissues have been studied using experimental QUS methods, and quantitative datasets are available in the literature. This educational review addresses the general topic of biological soft tissue characterization using QUS, with a focus on disseminating technical concepts for clinicians and specialized QUS materials for medical physicists. Advanced but simplified technical descriptions are also provided in separate subsections identified as such. To understand QUS methods, this article reviews types of ultrasound waves, basic concepts of ultrasound wave propagation, ultrasound image formation, point spread function, constructive and destructive wave interferences, radiofrequency data processing, and a summary of different imaging modes. For each major QUS technique, topics include: concept, illustrations, clinical examples, pitfalls, and future directions.
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Affiliation(s)
- Guy Cloutier
- Laboratory of Biorheology and Medical Ultrasonics, Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), 900 St-Denis, Montréal, Québec, H2X 0A9, Canada.
- Department of Radiology, Radio-oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada.
- Institute of Biomedical Engineering, Université de Montréal, Montréal, Québec, Canada.
| | - François Destrempes
- Laboratory of Biorheology and Medical Ultrasonics, Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), 900 St-Denis, Montréal, Québec, H2X 0A9, Canada
| | - François Yu
- Department of Radiology, Radio-oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada
- Institute of Biomedical Engineering, Université de Montréal, Montréal, Québec, Canada
- Microbubble Theranostics Laboratory, CRCHUM, Montréal, Québec, Canada
| | - An Tang
- Department of Radiology, Radio-oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada
- Department of Radiology, Centre hospitalier de l'Université de Montréal (CHUM), Montréal, Québec, Canada
- Laboratory of Medical Image Analysis, Montréal, CRCHUM, Canada
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17
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Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment. Sci Rep 2021; 11:14865. [PMID: 34290259 PMCID: PMC8295369 DOI: 10.1038/s41598-021-94004-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/25/2021] [Indexed: 01/02/2023] Open
Abstract
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.
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18
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Romeo V, Accardo G, Perillo T, Basso L, Garbino N, Nicolai E, Maurea S, Salvatore M. Assessment and Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparison of Imaging Modalities and Future Perspectives. Cancers (Basel) 2021; 13:cancers13143521. [PMID: 34298733 PMCID: PMC8303777 DOI: 10.3390/cancers13143521] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 06/30/2021] [Indexed: 02/06/2023] Open
Abstract
Neoadjuvant chemotherapy (NAC) is becoming the standard of care for locally advanced breast cancer, aiming to reduce tumor size before surgery. Unfortunately, less than 30% of patients generally achieve a pathological complete response and approximately 5% of patients show disease progression while receiving NAC. Accurate assessment of the response to NAC is crucial for subsequent surgical planning. Furthermore, early prediction of tumor response could avoid patients being overtreated with useless chemotherapy sections, which are not free from side effects and psychological implications. In this review, we first analyze and compare the accuracy of conventional and advanced imaging techniques as well as discuss the application of artificial intelligence tools in the assessment of tumor response after NAC. Thereafter, the role of advanced imaging techniques, such as MRI, nuclear medicine, and new hybrid PET/MRI imaging in the prediction of the response to NAC is described in the second part of the review. Finally, future perspectives in NAC response prediction, represented by AI applications, are discussed.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (T.P.); (S.M.)
- Correspondence: ; Tel.: +39-3930426928; Fax: +39-081-746356
| | - Giuseppe Accardo
- Department of Breast Surgery, Centro di Riferimento Oncologico della Basilicata (IRCCS-CROB), Rionero in Vulture, 85028 Potenza, Italy;
| | - Teresa Perillo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (T.P.); (S.M.)
| | - Luca Basso
- IRCCS SDN, 80143 Naples, Italy; (L.B.); (N.G.); (E.N.); (M.S.)
| | - Nunzia Garbino
- IRCCS SDN, 80143 Naples, Italy; (L.B.); (N.G.); (E.N.); (M.S.)
| | | | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (T.P.); (S.M.)
| | - Marco Salvatore
- IRCCS SDN, 80143 Naples, Italy; (L.B.); (N.G.); (E.N.); (M.S.)
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Lye TH, Roshankhah R, Karbalaeisadegh Y, Montgomery SA, Egan TM, Muller M, Mamou J. In vivo assessment of pulmonary fibrosis and edema in rodents using the backscatter coefficient and envelope statistics. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2021; 150:183. [PMID: 34340489 DOI: 10.1121/10.0005481] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/08/2021] [Indexed: 06/13/2023]
Abstract
Quantitative ultrasound methods based on the backscatter coefficient (BSC) and envelope statistics have been used to quantify disease in a wide variety of tissues, such as prostate, lymph nodes, breast, and thyroid. However, to date, these methods have not been investigated in the lung. In this study, lung properties were quantified by BSC and envelope statistical parameters in normal, fibrotic, and edematous rat lungs in vivo. The average and standard deviation of each parameter were calculated for each lung as well as the evolution of each parameter with acoustic propagation time within the lung. The transport mean free path and backscattered frequency shift, two parameters that have been successfully used to assess pulmonary fibrosis and edema in prior work, were evaluated in combination with the BSC and envelope statistical parameters. Multiple BSC and envelope statistical parameters were found to provide contrast between control and diseased lungs. BSC and envelope statistical parameters were also significantly correlated with fibrosis severity using the modified Ashcroft fibrosis score as the histological gold standard. These results demonstrate the potential for BSC and envelope statistical parameters to improve the diagnosis of pulmonary fibrosis and edema as well as monitor pulmonary fibrosis.
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Affiliation(s)
- Theresa H Lye
- F. L. Lizzi Center for Biomedical Engineering, Riverside Research, New York, New York 10038, USA
| | - Roshan Roshankhah
- Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Yasamin Karbalaeisadegh
- Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Stephanie A Montgomery
- Department of Pathology and Laboratory Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Thomas M Egan
- Division of Cardiothoracic Surgery, Dept. of Surgery, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Marie Muller
- Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Jonathan Mamou
- F. L. Lizzi Center for Biomedical Engineering, Riverside Research, New York, New York 10038, USA
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Li J, Wang H, Wang L, Wei T, Wu M, Li T, Liao J, Tan B, Lu M. The concordance in lesion detection and characteristics between the Anatomical Intelligence and conventional breast ultrasound Scan method. BMC Med Imaging 2021; 21:102. [PMID: 34154558 PMCID: PMC8215794 DOI: 10.1186/s12880-021-00628-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 05/24/2021] [Indexed: 11/17/2022] Open
Abstract
Background The aim of this study was to investigate the concordance in lesion detection, between conventional Handhold Ultrasound (HHUS) and The Anatomical Intelligence for Breast ultrasound scan method. Result The AI-breast showed the absolute agreement between the resident and an experienced breast radiologist. The ICC for the scan time, number, clockface location, distance to the nipple, largest diameter and mean diameter of the lesion obtained by a resident and an experienced breast radiologist were 0.7642, 0.7692, 0.8651, 0.8436, 0.7502, 0.8885, respectively. The ICC of the both practitioners of AI-breast were 0.7971, 0.7843, 0.9283, 0.8748, 0.7248, 0.8163, respectively. The k value of Anatomical Intelligence breast between experienced breast radiologist and resident in these image characteristics of boundary, morphology, aspect ratio, internal echo, and BI-RADS assessment were 0.7424, 0.7217, 0.6741, 0.6419, 0.6241, respectively. The k value of the two readers of AI-breast were 0.6531, 0.6762, 0.6439, 0.6137, 0.5981, respectively. Conclusion The anatomical intelligent breast US scanning method has excellent reproducibility in recording the lesion location and the distance from the nipple, which may be utilized in the lesions surveillance in the future.
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Affiliation(s)
- Juan Li
- Ultrasound Medical Center, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, China
| | - Hao Wang
- Breast Surgeons Center, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, China
| | - Lu Wang
- Ultrasound Medical Center, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, China
| | - Ting Wei
- Ultrasound Medical Center, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, China
| | - Minggang Wu
- Ultrasound Medical Center, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, China
| | - Tingting Li
- Ultrasound Medical Center, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, China
| | - Jifen Liao
- Ultrasound Medical Center, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, China
| | - Bo Tan
- Ultrasound Medical Center, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, China
| | - Man Lu
- Ultrasound Medical Center, Sichuan Cancer Hospital Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, China.
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21
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Yixin HMD, Fei LMD, Jianhua ZMD. Current Status and Advances in Imaging Evaluation of Neoadjuvant Chemotherapy of Breast Cancer. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY 2021. [DOI: 10.37015/audt.2021.190036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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22
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Osapoetra LO, Chan W, Tran W, Kolios MC, Czarnota GJ. Comparison of methods for texture analysis of QUS parametric images in the characterization of breast lesions. PLoS One 2020; 15:e0244965. [PMID: 33382837 PMCID: PMC7775053 DOI: 10.1371/journal.pone.0244965] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 12/18/2020] [Indexed: 01/06/2023] Open
Abstract
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.
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Affiliation(s)
- Laurentius O. Osapoetra
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - William Chan
- University of Waterloo, Toronto, Ontario, Canada
| | - William Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
- Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | | | - Gregory J. Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Physics, Ryerson University, Toronto, Ontario, Canada
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Duric N, Littrup P, Sak M, Li C, Chen D, Roy O, Bey-Knight L, Brem R. A Novel Marker, Based on Ultrasound Tomography, for Monitoring Early Response to Neoadjuvant Chemotherapy. JOURNAL OF BREAST IMAGING 2020; 2:569-576. [PMID: 33385161 DOI: 10.1093/jbi/wbaa084] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVE To evaluate the combination of tumor volume and sound speed as a potential imaging marker for assessing neoadjuvant chemotherapy (NAC) response. METHODS This study was carried out under an IRB-approved protocol (written consent required). Fourteen patients undergoing NAC for invasive breast cancer were examined with ultrasound tomography (UST) throughout their treatment. The volume (V) and the volume-averaged sound speed (VASS) of the tumors and their changes were measured for each patient. Time-dependent response curves of V and VASS were constructed individually for each patient and then as averages for the complete versus partial response groups in order to characterize differences between the two groups. Differences in group means were assessed for statistical significance using t-tests. Differences in shapes of group curves were evaluated with Kolmogorov-Smirnoff tests. RESULTS On average, tumor volume and sound speed in the partial response group showed a gradual decline in the first 60 days of treatment, while the complete response group showed a much steeper decline (P < 0.05). The shapes of the response curves of the two groups, corresponding to the entire treatment period, were also found to be significantly different (P < 0.05). Furthermore, large simultaneous drops in volume and sound speed in the first 3 weeks of treatment were characteristic only of the complete responders (P < 0.05). CONCLUSION This study demonstrates the feasibility of using UST to monitor NAC response, warranting future studies to better define the potential of UST for noninvasive, rapid identification of partial versus complete responders in women undergoing NAC.
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Affiliation(s)
- Neb Duric
- Delphinus Medical Technologies, Inc., Novi, MI.,Wayne State University, Barbara Ann Karmanos Cancer Institute, Department of Oncology, Detroit, MI
| | - Peter Littrup
- Delphinus Medical Technologies, Inc., Novi, MI.,Wayne State University, Barbara Ann Karmanos Cancer Institute, Department of Oncology, Detroit, MI
| | - Mark Sak
- Delphinus Medical Technologies, Inc., Novi, MI
| | - Cuiping Li
- Delphinus Medical Technologies, Inc., Novi, MI
| | - Di Chen
- Delphinus Medical Technologies, Inc., Novi, MI
| | - Olivier Roy
- Delphinus Medical Technologies, Inc., Novi, MI
| | - Lisa Bey-Knight
- Delphinus Medical Technologies, Inc., Novi, MI.,Wayne State University, Barbara Ann Karmanos Cancer Institute, Department of Oncology, Detroit, MI
| | - Rachel Brem
- George Washington University, Department of Radiology, Washington, DC
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Quiaoit K, DiCenzo D, Fatima K, Bhardwaj D, Sannachi L, Gangeh M, Sadeghi-Naini A, Dasgupta A, Kolios MC, Trudeau M, Gandhi S, Eisen A, Wright F, Look-Hong N, Sahgal A, Stanisz G, Brezden C, Dinniwell R, Tran WT, Yang W, Curpen B, Czarnota GJ. Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results. PLoS One 2020; 15:e0236182. [PMID: 32716959 PMCID: PMC7384762 DOI: 10.1371/journal.pone.0236182] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 06/30/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Neoadjuvant chemotherapy (NAC) is the standard of care for patients with locally advanced breast cancer (LABC). The study was conducted to investigate the utility of quantitative ultrasound (QUS) carried out during NAC to predict the final tumour response in a multi-institutional setting. METHODS Fifty-nine patients with LABC were enrolled from three institutions in North America (Sunnybrook Health Sciences Centre (Toronto, Canada), MD Anderson Cancer Centre (Texas, USA), and Princess Margaret Cancer Centre (Toronto, Canada)). QUS data were collected before starting NAC and subsequently at weeks 1 and 4 during chemotherapy. Spectral tumour parametric maps were generated, and textural features determined using grey-level co-occurrence matrices. Patients were divided into two groups based on their pathological outcomes following surgery: responders and non-responders. Machine learning algorithms using Fisher's linear discriminant (FLD), K-nearest neighbour (K-NN), and support vector machine (SVM-RBF) were used to generate response classification models. RESULTS Thirty-six patients were classified as responders and twenty-three as non-responders. Among all the models, SVM-RBF had the highest accuracy of 81% at both weeks 1 and week 4 with area under curve (AUC) values of 0.87 each. The inclusion of week 1 and 4 features led to an improvement of the classifier models, with the accuracy and AUC from baseline features only being 76% and 0.68, respectively. CONCLUSION QUS data obtained during NAC reflect the ongoing treatment-related changes during chemotherapy and can lead to better classifier performances in predicting the ultimate pathologic response to treatment compared to baseline features alone.
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Affiliation(s)
- Karina Quiaoit
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Daniel DiCenzo
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Kashuf Fatima
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Divya Bhardwaj
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Lakshmanan Sannachi
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Mehrdad Gangeh
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Ali Sadeghi-Naini
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada
| | - Archya Dasgupta
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | | | - Maureen Trudeau
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Sonal Gandhi
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Andrea Eisen
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Frances Wright
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Surgery, University of Toronto, Toronto, Canada
| | - Nicole Look-Hong
- Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Surgery, University of Toronto, Toronto, Canada
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Greg Stanisz
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Christine Brezden
- Department of Medical Oncology, Saint Michael's Hospital, University of Toronto, Toronto, Canada
| | - Robert Dinniwell
- Department of Radiation Oncology, Princess Margaret Hospital, University Health Network, Toronto, Canada
- Department of Radiation Oncology, London Health Sciences Centre, London, Canada
- Department of Oncology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - William T. Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Canada
| | - Wei Yang
- Department of Diagnostic Radiology, University of Texas, M.D. Anderson Cancer Center, Houston, Texas, United States of America
| | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Gregory J. Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada
- Department of Physics, Ryerson University, Toronto, Canada
- * E-mail:
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25
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Osapoetra LO, Sannachi L, DiCenzo D, Quiaoit K, Fatima K, Czarnota GJ. Breast lesion characterization using Quantitative Ultrasound (QUS) and derivative texture methods. Transl Oncol 2020; 13:100827. [PMID: 32663657 PMCID: PMC7358267 DOI: 10.1016/j.tranon.2020.100827] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 06/12/2020] [Indexed: 12/19/2022] Open
Abstract
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.
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Affiliation(s)
- Laurentius O Osapoetra
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada; Departments of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Lakshmanan Sannachi
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada; Departments of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Daniel DiCenzo
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Karina Quiaoit
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Kashuf Fatima
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Gregory J Czarnota
- Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada; Departments of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
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26
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Wiacek A, Oluyemi E, Myers K, Mullen L, Bell MAL. Coherence-Based Beamforming Increases the Diagnostic Certainty of Distinguishing Fluid from Solid Masses in Breast Ultrasound Exams. ULTRASOUND IN MEDICINE & BIOLOGY 2020; 46:1380-1394. [PMID: 32122720 DOI: 10.1016/j.ultrasmedbio.2020.01.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 01/16/2020] [Accepted: 01/20/2020] [Indexed: 05/23/2023]
Abstract
Ultrasound is often used as a supplement for mammography to detect breast cancer. However, one known limitation is the high false-positive rates associated with breast ultrasound. We investigated the use of coherence-based beamforming (which directly displays spatial coherence) as a supplement to standard ultrasound B-mode images in 25 patients recommended for biopsy (26 masses in total), with the eventual goal of decreasing false-positive rates. Because of the coherent signal present within solid masses, coherence-based beamforming methods allow solid and fluid-filled masses to appear significantly different (p < 0.001). When presented to five board-certified radiologists, the inclusion of robust short-lag spatial coherence (R-SLSC) images in the diagnostic pipeline reduced the uncertainty of fluid-filled mass contents from 47.5% to 15.8% and reduced the percentage of fluid-filled masses unnecessarily recommended for biopsy from 43.3% to 13.3%. These results are promising for the potential introduction of R-SLSC (and related coherence-based beamforming methods) into the breast clinic to improve diagnostic certainty and reduce the number of unnecessary biopsies.
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Affiliation(s)
- Alycen Wiacek
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
| | - Eniola Oluyemi
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Kelly Myers
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Lisa Mullen
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Muyinatu A Lediju Bell
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA; Department of Computer Science, John Hopkins University, Baltimore, Maryland, USA
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27
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Xiao Q, Zhou Y, Winter S, Büttner F, Schaeffeler E, Schwab M, Lauschke VM. Germline variant burden in multidrug resistance transporters is a therapy-specific predictor of survival in breast cancer patients. Int J Cancer 2020; 146:2475-2487. [PMID: 32010961 DOI: 10.1002/ijc.32898] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 01/27/2020] [Indexed: 12/12/2022]
Abstract
Multidrug resistance due to facilitated drug efflux mediated by ATP-binding cassette (ABC) transporters is a main cause for failure of cancer therapy. Genetic polymorphisms in ABC genes affect the disposition of chemotherapeutics and constitute important biomarkers for therapeutic response and toxicity. Here we correlated germline variability in ABC transporters with disease-specific survival (DSS) in 960 breast cancer (BRCA), 314 clear cell renal cell carcinoma and 325 hepatocellular carcinoma patients. We find that variant burden in ABCC1 is a strong predictor of DSS in BRCA patients, whereas candidate polymorphisms are not associated with DSS. This association is highly drug-specific for subgroups treated with the MRP1 substrates cyclophosphamide (log-rank p = 0.0011) and doxorubicin (log-rank p = 0.0088) independent of age and tumor stage, whereas no association was found in individuals treated with tamoxifen (log-rank p = 0.13). Structural mapping of significant variants revealed multiple variants at residues involved in protein stability, cofactor stabilization or substrate binding. Our results demonstrate that BRCA patients with high variant burden in ABCC1 are less prone to respond appropriately to pharmacological therapy with MRP1 substrates, thus incentivizing the consideration of genomic germline data for precision cancer medicine.
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MESH Headings
- Antineoplastic Combined Chemotherapy Protocols/therapeutic use
- Biomarkers, Tumor/genetics
- Breast Neoplasms/drug therapy
- Breast Neoplasms/genetics
- Breast Neoplasms/mortality
- Breast Neoplasms/pathology
- Carcinoma, Hepatocellular/drug therapy
- Carcinoma, Hepatocellular/genetics
- Carcinoma, Hepatocellular/mortality
- Carcinoma, Hepatocellular/pathology
- Carcinoma, Renal Cell/drug therapy
- Carcinoma, Renal Cell/genetics
- Carcinoma, Renal Cell/mortality
- Carcinoma, Renal Cell/pathology
- Cohort Studies
- Drug Resistance, Neoplasm/genetics
- Female
- Follow-Up Studies
- Germ-Line Mutation
- Humans
- Kidney Neoplasms/drug therapy
- Kidney Neoplasms/genetics
- Kidney Neoplasms/mortality
- Kidney Neoplasms/pathology
- Liver Neoplasms/drug therapy
- Liver Neoplasms/genetics
- Liver Neoplasms/mortality
- Liver Neoplasms/pathology
- Middle Aged
- Multidrug Resistance-Associated Proteins/genetics
- Prognosis
- Survival Rate
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Affiliation(s)
- Qingyang Xiao
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Yitian Zhou
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Stefan Winter
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany and University of Tuebingen, Tuebingen, Germany
| | - Florian Büttner
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany and University of Tuebingen, Tuebingen, Germany
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany and University of Tuebingen, Tuebingen, Germany
- iFIT Cluster of Excellence (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tuebingen, Tuebingen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany and University of Tuebingen, Tuebingen, Germany
- iFIT Cluster of Excellence (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tuebingen, Tuebingen, Germany
- Department of Clinical Pharmacology, Pharmacy and Biochemistry, University Tuebingen, Tuebingen, Germany
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
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Sannachi L, Gangeh M, Naini AS, Bhargava P, Jain A, Tran WT, Czarnota GJ. Quantitative Ultrasound Monitoring of Breast Tumour Response to Neoadjuvant Chemotherapy: Comparison of Results Among Clinical Scanners. ULTRASOUND IN MEDICINE & BIOLOGY 2020; 46:1142-1157. [PMID: 32111456 DOI: 10.1016/j.ultrasmedbio.2020.01.022] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 01/17/2020] [Accepted: 01/20/2020] [Indexed: 06/10/2023]
Abstract
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.
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Affiliation(s)
- Lakshmanan Sannachi
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Mehrdad Gangeh
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Ali-Sadeghi Naini
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Priya Bhargava
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Aparna Jain
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - William Tyler Tran
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Gregory Jan Czarnota
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada.
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Alnazer I, Falou O, Nasr R, Azar D, Hysi E, Wirtzfeld L, Berndl ESL, Kolios MC. Quantitative Ultrasound Imaging for the Differentiation between Fresh and Decellularized Mouse Kidneys .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:6624-6627. [PMID: 31947360 DOI: 10.1109/embc.2019.8856518] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Decellularization is a technique that permits the removal of cells from intact organs while preserving the extracellular matrix (ECM). It has many applications in various fields such as regenerative medicine and tissue engineering. This study aims to differentiate between fresh and decellularized kidneys using quantitative ultrasound (QUS) parameters. Spectral parameters were extracted from the linear fit of the power spectrum of raw radio frequency data and parametric maps were generated corresponding to the regions of interest, from which four textural parameters were estimated. The results of this study indicated that decellularization affects both spectral and textural parameters. The Mid Band Fit mean and contrast were found to be the best spectral and textural predictors of kidney decellularization, respectively.
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Predictive quantitative ultrasound radiomic markers associated with treatment response in head and neck cancer. Future Sci OA 2019; 6:FSO433. [PMID: 31915534 PMCID: PMC6920736 DOI: 10.2144/fsoa-2019-0048] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Aim: We aimed to identify quantitative ultrasound (QUS)-radiomic markers to predict radiotherapy response in metastatic lymph nodes of head and neck cancer. Materials & methods: Node-positive head and neck cancer patients underwent pretreatment QUS imaging of their metastatic lymph nodes. Imaging features were extracted using the QUS spectral form, and second-order texture parameters. Machine-learning classifiers were used for predictive modeling, which included a logistic regression, naive Bayes, and k-nearest neighbor classifiers. Results: There was a statistically significant difference in the pretreatment QUS-radiomic parameters between radiological complete responders versus partial responders (p < 0.05). The univariable model that demonstrated the greatest classification accuracy included: spectral intercept (SI)-contrast (area under the curve = 0.741). Multivariable models were also computed and showed that the SI-contrast + SI-homogeneity demonstrated an area under the curve = 0.870. The three-feature model demonstrated that the spectral slope-correlation + SI-contrast + SI-homogeneity-predicted response with accuracy of 87.5%. Conclusion: Multivariable QUS-radiomic features of metastatic lymph nodes can predict treatment response a priori. In this study, quantitative ultrasound (QUS) and machine-learning classification was used to predict treatment outcomes in head and neck cancer patients. Metastatic lymph nodes in the neck were scanned using conventional frequency ultrasound (US). Quantitative data were collected from the US-radiofrequency signal a priori. Machine-learning classification models were computed using QUS features; these included the linear fit parameters of the power spectrum, and second-order texture parameters of the QUS parametric images. Treatment outcomes were measured based on radiological response. Patients were classified into binary groups: radiologic complete response (CR) or radiological partial response (PR), which was assessed 3 months following treatment. Initial results demonstrate high accuracy (%Acc = 87.5%) for predicting radiological response. The results of this study suggest that QUS can be used to predict head and neck cancer response to radiotherapy a priori.
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Durot I, Sigrist RMS, Kothary N, Rosenberg J, Willmann JK, El Kaffas A. Quantitative Ultrasound Spectroscopy for Differentiation of Hepatocellular Carcinoma from At-Risk and Normal Liver Parenchyma. Clin Cancer Res 2019; 25:6683-6691. [PMID: 31444249 DOI: 10.1158/1078-0432.ccr-19-1030] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 05/23/2019] [Accepted: 08/20/2019] [Indexed: 11/16/2022]
Abstract
PURPOSE Quantitative ultrasound approaches can capture tissue morphologic properties to augment clinical diagnostics. This study aims to clinically assess whether quantitative ultrasound spectroscopy (QUS) parameters measured in hepatocellular carcinoma (HCC) tissues can be differentiated from those measured in at-risk or healthy liver parenchyma. EXPERIMENTAL DESIGN This prospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study was approved by the Institutional Review Board. Fifteen patients with HCC, 15 non-HCC patients with chronic liver disease, and 15 healthy volunteers were included (31.1% women; 68.9% men). Ultrasound radiofrequency data were acquired in each patient in both liver lobes at two focal depths (3/9 cm). Region of interests (ROIs) were drawn on HCC and liver parenchyma. The average normalized power spectrum for each ROI was extracted, and a linear regression was fit within the -6 dB bandwidth, from which the midband fit (MBF), spectral intercept (SI), and spectral slope (SS) were extracted. Differences in QUS parameters between the ROIs were tested by a mixed-effects regression. RESULTS There was a significant intraindividual difference in MBF, SS, and SI between HCC and adjacent liver parenchyma (P < 0.001), and a significant interindividual difference between HCC and at-risk and healthy non-HCC parenchyma (P < 0.001). In patients with HCC, cirrhosis (n = 13) did not significantly change any of the three parameters (P > 0.8) in differentiating HCC from non-HCC parenchyma. MBF (P = 0.12), SI (P = 0.33), and SS (P = 0.57) were not significantly different in non-HCC tissue among the groups. CONCLUSIONS The QUS parameters are significantly different in HCC versus non-HCC liver parenchyma, independent of underlying cirrhosis. This could be leveraged for improved HCC detection with ultrasound in the future.
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Affiliation(s)
- Isabelle Durot
- Department of Radiology, School of Medicine, Stanford University, Stanford, California.,Translational Molecular Imaging Lab, School of Medicine, Stanford University, Stanford, California
| | - Rosa M S Sigrist
- Department of Radiology, School of Medicine, Stanford University, Stanford, California.,Translational Molecular Imaging Lab, School of Medicine, Stanford University, Stanford, California
| | - Nishita Kothary
- Department of Radiology, School of Medicine, Stanford University, Stanford, California
| | - Jarrett Rosenberg
- Department of Radiology, School of Medicine, Stanford University, Stanford, California
| | - Jürgen K Willmann
- Department of Radiology, School of Medicine, Stanford University, Stanford, California.,Translational Molecular Imaging Lab, School of Medicine, Stanford University, Stanford, California
| | - Ahmed El Kaffas
- Department of Radiology, School of Medicine, Stanford University, Stanford, California. .,Translational Molecular Imaging Lab, School of Medicine, Stanford University, Stanford, California
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Klingensmith JD, Haggard AL, Ralston JT, Qiang B, Fedewa RJ, Elsharkawy H, Geoffrey Vince D. Tissue classification in intercostal and paravertebral ultrasound using spectral analysis of radiofrequency backscatter. J Med Imaging (Bellingham) 2019; 6:047001. [PMID: 31720315 DOI: 10.1117/1.jmi.6.4.047001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Accepted: 10/14/2019] [Indexed: 12/23/2022] Open
Abstract
Paravertebral and intercostal nerve blocks have experienced a resurgence in popularity. Ultrasound has become the gold standard for visualization of the needle during injection of the analgesic, but the intercostal artery and vein can be difficult to visualize. We investigated the use of spectral analysis of raw radiofrequency (RF) ultrasound signals for identification of the intercostal vessels and six other tissue types in the intercostal and paravertebral spaces. Features derived from the one-dimensional spectrum, two-dimensional spectrum, and cepstrum were used to train four different machine learning algorithms. In addition, the use of the average normalized spectrum as the feature set was compared with the derived feature set. Compared to a support vector machine (SVM) (74.2%), an artificial neural network (ANN) (68.2%), and multinomial analysis (64.1%), a random forest (84.9%) resulted in the most accurate classification. The accuracy using a random forest trained with the first 15 principal components of the average normalized spectrum was 87.0%. These results demonstrate that using a machine learning algorithm with spectral analysis of raw RF ultrasound signals has the potential to provide tissue characterization in intercostal and paravertebral ultrasound.
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Affiliation(s)
- Jon D Klingensmith
- Southern Illinois University Edwardsville, Department of Electrical and Computer Engineering, Edwardsville, Illinois, United States
| | - Asher L Haggard
- Southern Illinois University Edwardsville, Department of Electrical and Computer Engineering, Edwardsville, Illinois, United States
| | - Jack T Ralston
- Southern Illinois University Edwardsville, Department of Electrical and Computer Engineering, Edwardsville, Illinois, United States
| | - Beidi Qiang
- Southern Illinois University Edwardsville, Department of Mathematics and Statistics, Edwardsville, Illinois, United States
| | - Russell J Fedewa
- Cleveland Clinic Foundation, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Hesham Elsharkawy
- Cleveland Clinic Foundation, Department of General Anesthesia and Pain Management, Outcomes Research, and Anesthesiology Institute, Cleveland, Ohio, United States
| | - David Geoffrey Vince
- Cleveland Clinic Foundation, Department of Biomedical Engineering, Cleveland, Ohio, United States
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Zeng Y, Nandy S, Rao B, Li S, Hagemann AR, Kuroki LK, McCourt C, Mutch DG, Powell MA, Hagemann IS, Zhu Q. Histogram analysis of en face scattering coefficient map predicts malignancy in human ovarian tissue. JOURNAL OF BIOPHOTONICS 2019; 12:e201900115. [PMID: 31304678 PMCID: PMC7982142 DOI: 10.1002/jbio.201900115] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 06/22/2019] [Accepted: 07/11/2019] [Indexed: 05/18/2023]
Abstract
Ovarian cancer is a heterogeneous disease at the molecular and histologic level. Optical coherence tomography (OCT) is able to map ovarian tissue optical properties and heterogeneity, which has been proposed as a feature to aid in diagnosis of ovarian cancer. In this manuscript, depth-resolved en face scattering maps of malignant ovaries, benign ovaries, and benign fallopian tubes obtained from 20 patients are provided to visualize the heterogeneity of ovarian tissues. Six features are extracted from histograms of scattering maps. All features are able to statistically distinguish benign from malignant ovaries. Two prediction models were constructed based on these features: a logistic regression model (LR) and a support vector machine (SVM). The optimal set of features is mean scattering coefficient and scattering map entropy. The LR achieved a sensitivity and specificity of 97.0% and 97.8%, and SVM demonstrated a sensitivity and specificity of 99.6% and 96.4%. Our initial results demonstrate the feasibility of using OCT as an "optical biopsy tool" for detecting the microscopic scattering changes associated with neoplasia in human ovarian tissue.
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Affiliation(s)
- Yifeng Zeng
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Sreyankar Nandy
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Bin Rao
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Shuying Li
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
| | - Andrea R. Hagemann
- Department of Obstetrics & Gynecology, Washington University School of Medicine, St. Louis, Missouri
| | - Lindsay K. Kuroki
- Department of Obstetrics & Gynecology, Washington University School of Medicine, St. Louis, Missouri
| | - Carolyn McCourt
- Department of Obstetrics & Gynecology, Washington University School of Medicine, St. Louis, Missouri
| | - David G. Mutch
- Department of Obstetrics & Gynecology, Washington University School of Medicine, St. Louis, Missouri
| | - Matthew A. Powell
- Department of Obstetrics & Gynecology, Washington University School of Medicine, St. Louis, Missouri
| | - Ian S. Hagemann
- Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, Missouri
- Department of Obstetrics & Gynecology, Washington University School of Medicine, St. Louis, Missouri
| | - Quing Zhu
- Department of Biomedical Engineering, Washington University, St. Louis, Missouri
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Correspondence Dr. Quing Zhu, Department of Biomedical Engineering, Washington University, St. Louis, MO 63110.
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Fadhel MN, Hysi E, Zalev J, Kolios MC. Photoacoustic simulations of microvascular bleeding: spectral analysis and its application for monitoring vascular-targeted treatments. JOURNAL OF BIOMEDICAL OPTICS 2019; 24:1-8. [PMID: 31707772 PMCID: PMC7003142 DOI: 10.1117/1.jbo.24.11.116001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 10/21/2019] [Indexed: 05/04/2023]
Abstract
Solid tumors are typically supplied nutrients by a network of irregular blood vessels. By targeting these vascular networks, it might be possible to hinder cancer growth and metastasis. Vascular disrupting agents induce intertumoral hemorrhaging, making photoacoustic (PA) imaging well positioned to detect bleeding due to its sensitivity to hemoglobin and its various states. We introduce a fractal-based numerical model of intertumoral hemorrhaging to simulate the PA signals from disrupted tumor blood vessels. The fractal model uses bifurcated cylinders to represent vascular trees. To mimic bleeding from blood vessels, hemoglobin diffusion from microvessels was simulated. In the simulations, the PA signals were detected by a linear array transducer (30 MHz center frequency) of four different vascular trees. The power spectrum of each beamformed PA signal was computed and fitted to a straight line within the −6-dB bandwidth of the receiving transducer. The spectral slope and midband fit (MBF) based on the fit decreased by 0.11 dB / MHz and 2.12 dB, respectively, 1 h post bleeding, while the y-intercept increased by 1.21 dB. The results suggest that spectral PA analysis can be used to measure changes in the concentration and spatial distribution of hemoglobin in tissue without the need to resolve individual vessels. The simulations support the feasibility of using PA imaging and spectral analysis in cancer treatment monitoring by detecting microvessel disruption.
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Affiliation(s)
- Muhannad N. Fadhel
- Ryerson University, Department of Physics, Toronto, Canada
- Institute for Biomedical Engineering, Science and Technology, St. Michael’s Hospital, Keenan Research Center, Toronto, Canada
| | - Eno Hysi
- Ryerson University, Department of Physics, Toronto, Canada
- Institute for Biomedical Engineering, Science and Technology, St. Michael’s Hospital, Keenan Research Center, Toronto, Canada
| | - Jason Zalev
- Ryerson University, Department of Physics, Toronto, Canada
- Institute for Biomedical Engineering, Science and Technology, St. Michael’s Hospital, Keenan Research Center, Toronto, Canada
| | - Michael C. Kolios
- Ryerson University, Department of Physics, Toronto, Canada
- Institute for Biomedical Engineering, Science and Technology, St. Michael’s Hospital, Keenan Research Center, Toronto, Canada
- Address all correspondence to Michael C. Kolios, E-mail:
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Fernandes J, Sannachi L, Tran WT, Koven A, Watkins E, Hadizad F, Gandhi S, Wright F, Curpen B, El Kaffas A, Faltyn J, Sadeghi-Naini A, Czarnota G. Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Ultrasound Strain Elastography. Transl Oncol 2019; 12:1177-1184. [PMID: 31226518 PMCID: PMC6586920 DOI: 10.1016/j.tranon.2019.05.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 05/03/2019] [Accepted: 05/06/2019] [Indexed: 02/06/2023] Open
Abstract
Strain elastography was used to monitor response to neoadjuvant chemotherapy (NAC) in 92 patients with biopsy-proven, locally advanced breast cancer. Strain elastography data were collected before, during, and after NAC. Relative changes in tumor strain ratio (SR) were calculated over time, and responder status was classified according to tumor size changes. Statistical analyses determined the significance of changes in SR over time and between response groups. Machine learning techniques, such as a naïve Bayes classifier, were used to evaluate the performance of the SR as a marker for Miller-Payne pathological endpoints. With pathological complete response (pCR) as an endpoint, a significant difference (P < .01) in the SR was observed between response groups as early as 2 weeks into NAC. Naïve Bayes classifiers predicted pCR with a sensitivity of 84%, specificity of 85%, and area under the curve of 81% at the preoperative scan. This study demonstrates that strain elastography may be predictive of NAC response in locally advanced breast cancer as early as 2 weeks into treatment, with high sensitivity and specificity, granting it the potential to be used for active monitoring of tumor response to chemotherapy.
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Affiliation(s)
- Jason Fernandes
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA
| | - Lakshmanan Sannachi
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA; Physical Sciences, Sunnybrook Research Institute, Toronto, CA
| | - William T Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA; Department of Radiation Oncology, University of Toronto, Toronto, CA; Centre for Health and Social Care Research, Sheffield Hallam University, Sheffield, UK; Institute of Clinical Evaluative Sciences, Sunnybrook Research Institute, Toronto, CA
| | - Alexander Koven
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA
| | - Elyse Watkins
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA
| | - Farnoosh Hadizad
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA
| | - Sonal Gandhi
- Division of Medical Oncology, Sunnybrook Health Sciences Centre, Toronto, CA
| | - Frances Wright
- Division of Surgical Oncology, Sunnybrook Health Sciences Centre, Toronto, CA
| | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, CA
| | - Ahmed El Kaffas
- Physical Sciences, Sunnybrook Research Institute, Toronto, CA
| | - Joanna Faltyn
- Physical Sciences, Sunnybrook Research Institute, Toronto, CA
| | - Ali Sadeghi-Naini
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA; Department of Radiation Oncology, University of Toronto, Toronto, CA; Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, CA; Physical Sciences, Sunnybrook Research Institute, Toronto, CA
| | - Gregory Czarnota
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, CA; Department of Radiation Oncology, University of Toronto, Toronto, CA; Department of Medical Biophysics, University of Toronto, Toronto, CA; Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, CA; Physical Sciences, Sunnybrook Research Institute, Toronto, CA.
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Li F, Huang Y, Wang J, Lin C, Li Q, Zheng X, Wang Y, Cao L, Zhou J. Early differentiating between the chemotherapy responders and nonresponders: preliminary results with ultrasonic spectrum analysis of the RF time series in preclinical breast cancer models. Cancer Imaging 2019; 19:61. [PMID: 31462322 PMCID: PMC6714306 DOI: 10.1186/s40644-019-0248-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 08/14/2019] [Indexed: 11/30/2022] Open
Abstract
Background This study was aimed to assess whether ultrasonic spectrum analysis of radiofrequency (RF) time series using a clinical ultrasound system allows for early differentiating between the chemotherapy responders and nonresponders in human breast cancer xenografts that imitate clinical responding and nonresponding tumors. Methods Clinically responding (n = 20; MCF-7) and nonresponding (n = 20; MBA-MD-231) breast cancer xenografts were established in 40 nude mice. Ten mice from each group received either chemotherapy (adriamycin, 4 mg/kg) or saline as controls. Each tumor was imaged longitudinally with a clinical ultrasound scanner at baseline (day 0) and subsequently on days 2, 4, 6, 8 and 12 following treatment, and the corresponding RF time-series data were collected. Changes in six RF time-series parameters (slope, intercept, S1, S2, S3 and S4) were compared with the measurement of the tumor cell density, and their differential performances of the treatment response were analyzed. Results Adriamycin significantly inhibited tumor growth and decreased the cancer cell density in responders (P < 0.001) but not in nonresponders (P > 0.05). Fold changes of slope were significantly increased in responders two days after adriamycin treatment (P = 0.002), but not in nonresponders (P > 0.05). Early changes in slope on day 2 could differentiate the treatment response in 100% of both responders (95% CI, 62.9–100.0%) and nonresponders (95% CI, 88.4–100%). Conclusions Ultrasonic RF time series allowed for the monitoring of the tumor response to chemotherapy and could further serve as biomarkers for early differentiating between the treatment responders and nonresponders. Electronic supplementary material The online version of this article (10.1186/s40644-019-0248-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Fei Li
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China
| | - Yini Huang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China
| | - Jianwei Wang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China
| | - Chunyi Lin
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510640, People's Republic of China
| | - Qing Li
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China
| | - Xueyi Zheng
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China
| | - Yun Wang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China
| | - Longhui Cao
- Department of Anesthesiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China.
| | - Jianhua Zhou
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, People's Republic of China.
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Behr M, Noseworthy M, Kumbhare D. Feasibility of a Support Vector Machine Classifier for Myofascial Pain Syndrome: Diagnostic Case-Control Study. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2019; 38:2119-2132. [PMID: 30614553 DOI: 10.1002/jum.14909] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 11/09/2018] [Accepted: 11/26/2018] [Indexed: 06/09/2023]
Abstract
OBJECTIVES Myofascial pain syndrome (MPS) is the most common cause of chronic pain worldwide. The diagnosis of MPS is subjective, which has created a need for a robust quantitative method of diagnosing MPS. We propose that using a support vector machine (SVM) along with ultrasound (US) texture features can differentiate between healthy and MPS-affected skeletal muscle. METHODS B-mode US video data were collected in the upper trapezius muscle of healthy (29) participants and patients with active (21) and latent (19) MPS, using an acquisition method outlined in previous works. Regions of interest were extracted and filtered to obtain a unique set of 917 images where texture features were extracted from each region of interest to characterize each image. These texture features were then used to train 4 separate binary SVM classifiers using nested cross-validation to implement feature selection and hyperparameter tuning. The performance of each kernel was estimated on the data and validated through testing on a final holdout set. RESULTS The radial basis function kernel classifier had the greatest Matthews correlation coefficient performance estimate of 0.627 ± 0.073 (mean ± SD) along with the largest area under the curve of 91.0% ± 3.0%. The final holdout test for the radial basis function classifier resulted in 86.96 accuracy, a Matthews correlation coefficient of 0.724, 88% sensitivity, and 86% specificity, validating our earlier performance estimates. CONCLUSIONS We have demonstrated that specific US texture features that have been used in other computer-aided diagnostic literature are feasible to use for the classification of healthy and MPS muscle using a binary SVM classifier.
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Affiliation(s)
- Michael Behr
- Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Toronto, Toronto, Ontario, Canada
| | - Michael Noseworthy
- McMaster School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada
- Departments of Radiology, McMaster University, Hamilton, Ontario, Canada
- Departments of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada
- Imaging Research Center, St Joseph's Healthcare, Hamilton, Ontario, Canada
| | - Dinesh Kumbhare
- Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Toronto, Toronto, Ontario, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
- McMaster School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada
- Imaging Research Center, St Joseph's Healthcare, Hamilton, Ontario, Canada
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Dobruch-Sobczak K, Piotrzkowska-Wróblewska H, Klimoda Z, Secomski W, Karwat P, Markiewicz-Grodzicka E, Kolasińska-Ćwikła A, Roszkowska-Purska K, Litniewski J. Monitoring the response to neoadjuvant chemotherapy in patients with breast cancer using ultrasound scattering coefficient: A preliminary report. J Ultrason 2019; 19:89-97. [PMID: 31355579 PMCID: PMC6750328 DOI: 10.15557/jou.2019.0013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 04/10/2019] [Indexed: 11/22/2022] Open
Abstract
Objective: Neoadjuvant chemotherapy was initially used in locally advanced breast cancer, and currently it is recommended for patients with Stage 3 and with early-stage disease with human epidermal growth factor receptors positive or triple-negative breast cancer. Ultrasound imaging in combination with a quantitative ultrasound method is a novel diagnostic approach. Aim of study: The aim of this study was to analyze the variability of the integrated backscatter coefficient, and to evaluate their use to predict the effectiveness of treatment and compare to ultrasound examination results. Material and method: Ten patients (mean age 52.9) with 13 breast tumors (mean dimension 41 mm) were selected for neoadjuvant chemotherapy. Ultrasound was performed before the treatment and one week after each course of neoadjuvant chemotherapy. The dimensions were assessed adopting the RECIST criteria. Tissue responses were classified as pathological response into the following categories: not responded to the treatment (G1, cell reduction by ≤9%) and responded to the treatment partially: G2, G3, G4, cell reduction by 10-29% (G2), 30-90% (G3), >90% (G4), respectively, and completely. Results: In B-mode examination partial response was observed in 9/13 cases (completely, G1, G3, G4), and stable disease was demonstrated in 3/13 cases (completely, G1, G4). Complete response was found in 1/13 cases. As for backscatter coefficient, 10/13 tumors (completely, and G2, G3, and G4) were characterized by an increased mean value of 153%. Three tumors 3/13 (G1) displayed a decreased mean value of 31%. Conclusion: The variability of backscatter coefficient, could be associated with alterations in the structure of the tumor tissue during neoadjuvant chemotherapy. There were unequivocal differences between responded and non-responded patients. The backscatter coefficient analysis correlated better with the results of histopathological verification than with the B-mode RECIST criteria. Objective: Neoadjuvant chemotherapy was initially used in locally advanced breast cancer, and currently it is recommended for patients with Stage 3 and with early-stage disease with human epidermal growth factor receptors positive or triple-negative breast cancer. Ultrasound imaging in combination with a quantitative ultrasound method is a novel diagnostic approach. Aim of study: The aim of this study was to analyze the variability of the integrated backscatter coefficient, and to evaluate their use to predict the effectiveness of treatment and compare to ultrasound examination results. Material and method: Ten patients (mean age 52.9) with 13 breast tumors (mean dimension 41 mm) were selected for neoadjuvant chemotherapy. Ultrasound was performed before the treatment and one week after each course of neoadjuvant chemotherapy. The dimensions were assessed adopting the RECIST criteria. Tissue responses were classified as pathological response into the following categories: not responded to the treatment (G1, cell reduction by ≤9%) and responded to the treatment partially: G2, G3, G4, cell reduction by 10–29% (G2), 30–90% (G3), >90% (G4), respectively, and completely. Results: In B-mode examination partial response was observed in 9/13 cases (completely, G1, G3, G4), and stable disease was demonstrated in 3/13 cases (completely, G1, G4). Complete response was found in 1/13 cases. As for backscatter coefficient, 10/13 tumors (completely, and G2, G3, and G4) were characterized by an increased mean value of 153%. Three tumors 3/13 (G1) displayed a decreased mean value of 31%. Conclusion: The variability of backscatter coefficient, could be associated with alterations in the structure of the tumor tissue during neoadjuvant chemotherapy. There were unequivocal differences between responded and non-responded patients. The backscatter coefficient analysis correlated better with the results of histopathological verification than with the B-mode RECIST criteria.
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Affiliation(s)
- Katarzyna Dobruch-Sobczak
- Ultrasound Department , Institute of Fundamental Technological Research , Polish Academy of Sciences , Warsaw , Poland ; Radiology Department , M. Skłodowska-Curie Memorial Cancer Center and Institute of Oncology , Warsaw , Poland
| | - Hanna Piotrzkowska-Wróblewska
- Ultrasound Department , Institute of Fundamental Technological Research , Polish Academy of Sciences , Warsaw , Poland
| | - Ziemowit Klimoda
- Ultrasound Department , Institute of Fundamental Technological Research , Polish Academy of Sciences , Warsaw , Poland
| | - Wojciech Secomski
- Ultrasound Department , Institute of Fundamental Technological Research , Polish Academy of Sciences , Warsaw , Poland
| | - Piotr Karwat
- Ultrasound Department , Institute of Fundamental Technological Research , Polish Academy of Sciences , Warsaw , Poland
| | - Ewa Markiewicz-Grodzicka
- Chemiotherapy Department , M. Skłodowska-Curie Memorial Cancer Center and Institute of Oncology , Warsaw , Poland
| | - Agnieszka Kolasińska-Ćwikła
- Chemiotherapy Department , M. Skłodowska-Curie Memorial Cancer Center and Institute of Oncology , Warsaw , Poland
| | - Katarzyna Roszkowska-Purska
- Zakład Patomorfologii , M. Skłodowska-Curie Memorial Cancer Center and Institute of Oncology , Warsaw , Poland
| | - Jerzy Litniewski
- Ultrasound Department , Institute of Fundamental Technological Research , Polish Academy of Sciences , Warsaw , Poland
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Sannachi L, Gangeh M, Tadayyon H, Gandhi S, Wright FC, Slodkowska E, Curpen B, Sadeghi-Naini A, Tran W, Czarnota GJ. Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models. Transl Oncol 2019; 12:1271-1281. [PMID: 31325763 PMCID: PMC6639683 DOI: 10.1016/j.tranon.2019.06.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 06/12/2019] [Accepted: 06/17/2019] [Indexed: 12/31/2022] Open
Abstract
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.
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Affiliation(s)
- Lakshmanan Sannachi
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Mehrdad Gangeh
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Hadi Tadayyon
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Sonal Gandhi
- Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Frances C Wright
- Surgical Oncology, Department of General Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Elzbieta Slodkowska
- Department of Anatomic Pathology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Belinda Curpen
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Ali Sadeghi-Naini
- Department of Electrical Engineering & Computer Science, York University, Toronto, ON, Canada
| | - William Tran
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Gregory J Czarnota
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
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Perez-Moreno A, Dominguez M, Migliorelli F, Gratacos E, Palacio M, Bonet-Carne E. Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2019; 38:1459-1476. [PMID: 30269384 DOI: 10.1002/jum.14824] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 08/20/2018] [Indexed: 06/08/2023]
Abstract
OBJECTIVES To compare the robustness of several methods based on quantitative ultrasound (US) texture analysis to evaluate its feasibility for extracting features from US images to use as a clinical diagnostic tool. METHODS We compared, ranked, and validated the robustness of 5 texture-based methods for extracting textural features from US images acquired under different conditions. For comparison and ranking purposes, we used 13,171 non-US images from widely known available databases (OUTEX [University of Oulu, Oulu, Finland] and PHOTEX [Texture Lab, Heriot-Watt University, Edinburgh, Scotland]), which were specifically acquired under different controlled parameters (illumination, resolution, and rotation) from 103 textures. The robustness of those methods with better results from the non-US images was validated by using 666 fetal lung US images acquired from singleton pregnancies. In this study, 2 similarity measurements (correlation and Chebyshev distances) were used to evaluate the repeatability of the features extracted from the same tissue images. RESULTS Three of the 5 methods (gray-level co-occurrence matrix, local binary patterns, and rotation-invariant local phase quantization) had favorably robust performance when using the non-US database. In fact, these methods showed similarity values close to 0 for the acquisition variations and delineations. Results from the US database confirmed robustness for all of the evaluated methods (gray-level co-occurrence matrix, local binary patterns, and rotation-invariant local phase quantization) when comparing the same texture obtained from different regions of the image (proximal/distal lungs and US machine brand stratification). CONCLUSIONS Our results confirmed that texture analysis can be robust (high similarity for different condition acquisitions) with potential to be included as a clinical tool.
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Affiliation(s)
- Alvaro Perez-Moreno
- Transmural Biotech SL, Barcelona, Spain
- Fetal I + D Fetal Medicine Research Center, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | | | - Federico Migliorelli
- Fetal I + D Fetal Medicine Research Center, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
- BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Deu, Barcelona, Spain
| | - Eduard Gratacos
- Fetal I + D Fetal Medicine Research Center, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
- BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Deu, Barcelona, Spain
- Center for Biomedical Research on Rare Diseases, Barcelona, Spain
| | - Montse Palacio
- Fetal I + D Fetal Medicine Research Center, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
- BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine, Hospital Clínic and Hospital Sant Joan de Deu, Barcelona, Spain
- Center for Biomedical Research on Rare Diseases, Barcelona, Spain
| | - Elisenda Bonet-Carne
- Transmural Biotech SL, Barcelona, Spain
- University College London, Microstructure Imaging Group, Center for Medical Image Computing, London, England
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Strohm EM, Gnyawali V, Sebastian JA, Ngunjiri R, Moore MJ, Tsai SSH, Kolios MC. Sizing biological cells using a microfluidic acoustic flow cytometer. Sci Rep 2019; 9:4775. [PMID: 30886171 PMCID: PMC6423196 DOI: 10.1038/s41598-019-40895-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 02/25/2019] [Indexed: 12/19/2022] Open
Abstract
We describe a new technique that combines ultrasound and microfluidics to rapidly size and count cells in a high-throughput and label-free fashion. Using 3D hydrodynamic flow focusing, cells are streamed single file through an ultrasound beam where ultrasound scattering events from each individual cell are acquired. The ultrasound operates at a center frequency of 375 MHz with a wavelength of 4 μm; when the ultrasound wavelength is similar to the size of a scatterer, the power spectra of the backscattered ultrasound waves have distinct features at specific frequencies that are directly related to the cell size. Our approach determines cell sizes through a comparison of these distinct spectral features with established theoretical models. We perform an analysis of two types of cells: acute myeloid leukemia cells, where 2,390 measurements resulted in a mean size of 10.0 ± 1.7 μm, and HT29 colorectal cancer cells, where 1,955 measurements resulted in a mean size of 15.0 ± 2.3 μm. These results and histogram distributions agree very well with those measured from a Coulter Counter Multisizer 4. Our technique is the first to combine ultrasound and microfluidics to determine the cell size with the potential for multi-parameter cellular characterization using fluorescence, light scattering and quantitative photoacoustic techniques.
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Affiliation(s)
- Eric M Strohm
- Department of Physics, Ryerson University, 350 Victoria St, Toronto, Canada
- Institute for Biomedical Engineering and Science Technology, a partnership between Ryerson University and St. Michael's Hospital, M5B 1W8, Toronto, Canada
- Keenan Research Center for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael's Hospital, M5B 1W8, Toronto, Canada
| | - Vaskar Gnyawali
- Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria St, Toronto, Canada
- Institute for Biomedical Engineering and Science Technology, a partnership between Ryerson University and St. Michael's Hospital, M5B 1W8, Toronto, Canada
- Keenan Research Center for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael's Hospital, M5B 1W8, Toronto, Canada
| | - Joseph A Sebastian
- Department of Physics, Ryerson University, 350 Victoria St, Toronto, Canada
- Institute for Biomedical Engineering and Science Technology, a partnership between Ryerson University and St. Michael's Hospital, M5B 1W8, Toronto, Canada
- Keenan Research Center for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael's Hospital, M5B 1W8, Toronto, Canada
| | - Robert Ngunjiri
- Department of Physics, Ryerson University, 350 Victoria St, Toronto, Canada
- Institute for Biomedical Engineering and Science Technology, a partnership between Ryerson University and St. Michael's Hospital, M5B 1W8, Toronto, Canada
- Keenan Research Center for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael's Hospital, M5B 1W8, Toronto, Canada
| | - Michael J Moore
- Department of Physics, Ryerson University, 350 Victoria St, Toronto, Canada
- Institute for Biomedical Engineering and Science Technology, a partnership between Ryerson University and St. Michael's Hospital, M5B 1W8, Toronto, Canada
- Keenan Research Center for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael's Hospital, M5B 1W8, Toronto, Canada
| | - Scott S H Tsai
- Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria St, Toronto, Canada
- Institute for Biomedical Engineering and Science Technology, a partnership between Ryerson University and St. Michael's Hospital, M5B 1W8, Toronto, Canada
- Keenan Research Center for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael's Hospital, M5B 1W8, Toronto, Canada
| | - Michael C Kolios
- Department of Physics, Ryerson University, 350 Victoria St, Toronto, Canada.
- Institute for Biomedical Engineering and Science Technology, a partnership between Ryerson University and St. Michael's Hospital, M5B 1W8, Toronto, Canada.
- Keenan Research Center for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael's Hospital, M5B 1W8, Toronto, Canada.
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Dobruch-Sobczak K, Piotrzkowska-Wróblewska H, Klimonda Z, Roszkowska-Purska K, Litniewski J. Ultrasound echogenicity reveals the response of breast cancer to chemotherapy. Clin Imaging 2019; 55:41-46. [PMID: 30739033 DOI: 10.1016/j.clinimag.2019.01.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Revised: 12/16/2018] [Accepted: 01/25/2019] [Indexed: 01/09/2023]
Abstract
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.
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Affiliation(s)
- Katarzyna Dobruch-Sobczak
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Pawińskiego 5B, 02-106, Poland; Department of Ultrasound, Cancer Center and Institute of Oncology, M. Skłodowska-Curie Memorial, Wawelska 15, 02-034 Warsaw, Poland.
| | - Hanna Piotrzkowska-Wróblewska
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Pawińskiego 5B, 02-106, Poland
| | - Ziemowit Klimonda
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Pawińskiego 5B, 02-106, Poland
| | - Katarzyna Roszkowska-Purska
- Department of Pathology, Cancer Center and Institute of Oncology, M. Skłodowska-Curie Memorial, Warsaw, Poland
| | - Jerzy Litniewski
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Pawińskiego 5B, 02-106, Poland
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Han A, Labyed Y, Sy EZ, Boehringer AS, Andre MP, Erdman JW, Loomba R, Sirlin CB, O'Brien WD. Inter-sonographer reproducibility of quantitative ultrasound outcomes and shear wave speed measured in the right lobe of the liver in adults with known or suspected non-alcoholic fatty liver disease. Eur Radiol 2018; 28:4992-5000. [PMID: 29869170 PMCID: PMC7235946 DOI: 10.1007/s00330-018-5541-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 04/27/2018] [Accepted: 05/14/2018] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To assess inter-sonographer reproducibility of ultrasound attenuation coefficient (AC), backscatter coefficient (BSC) and shear wave speed (SWS) in adults with known/suspected non-alcoholic fatty liver disease (NAFLD). METHODS The institutional review board approved this HIPAA-compliant prospective study; informed consent was obtained. Participants with known/suspected NAFLD were recruited and underwent same-day liver examinations with a clinical scanner. Each participant was scanned by two of the six trained sonographers. Each sonographer performed multiple data acquisitions in the right liver lobe using a lateral intercostal approach. A data acquisition was a single operator button press that recorded a B-mode image, radio-frequency data, and the SWS value. AC and BSC were calculated from the radio-frequency data using the reference phantom method. SWS was calculated automatically using product software. Intraclass correlation coefficient (ICC) and within-subject coefficient of variation (wCV) were calculated for applicable metrics. RESULTS Sixty-one participants were recruited. Inter-sonographer ICC was 0.86 (95% confidence interval: 0.77-0.92) for AC and 0.87 (0.78-0.92) for log-transformed BSC (logBSC = 10log10BSC) using one acquisition per sonographer. ICC was 0.88 (0.80-0.93) for both AC and logBSC averaging 5 acquisitions. ICC for SWS was 0.57 (0.29-0.74) using one acquisition per sonographer, and 0.84 (0.66-0.93) using 10 acquisitions. The wCV was ~7% for AC, and 19-43% for SWS, depending on number of acquisitions. CONCLUSIONS Hepatic AC, BSC and SWS measures on a clinical scanner have good inter-sonographer reproducibility in adults with known or suspected NAFLD. Multiple acquisitions are required for SWS but not AC or BSC to achieve good inter-sonographer reproducibility. KEY POINTS • AC, BSC and SWS measurements are reproducible in adults with NAFLD. • Inter-sonographer reproducibility of SWS measurement improves with more acquisitions being averaged. • Multiple acquisitions are required for SWS but not AC or BSC.
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Affiliation(s)
- Aiguo Han
- Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 306 North Wright Street, Urbana, IL, 61801, USA
| | - Yassin Labyed
- Siemens Healthineers USA, 22010 South East 51st Street, Issaquah, WA, 98029, USA
| | - Ethan Z Sy
- Liver Imaging Group, Department of Radiology, University of California at San Diego, 9452 Medical Center Drive, La Jolla, CA, 92037, USA
| | - Andrew S Boehringer
- Liver Imaging Group, Department of Radiology, University of California at San Diego, 9452 Medical Center Drive, La Jolla, CA, 92037, USA
| | - Michael P Andre
- Department of Radiology, University of California at San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA
- San Diego VA Healthcare System, San Diego, CA, USA
| | - John W Erdman
- Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, 905 South Goodwin Avenue, Urbana, IL, 61801, USA
| | - Rohit Loomba
- NAFLD Research Center, Division of Gastroenterology, Department of Medicine, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Claude B Sirlin
- Liver Imaging Group, Department of Radiology, University of California at San Diego, 9452 Medical Center Drive, La Jolla, CA, 92037, USA
| | - William D O'Brien
- Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 306 North Wright Street, Urbana, IL, 61801, USA.
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Han A, Andre MP, Deiranieh L, Housman E, Erdman JW, Loomba R, Sirlin CB, O’Brien WD. Repeatability and Reproducibility of the Ultrasonic Attenuation Coefficient and Backscatter Coefficient Measured in the Right Lobe of the Liver in Adults With Known or Suspected Nonalcoholic Fatty Liver Disease. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2018; 37:1913-1927. [PMID: 29359454 PMCID: PMC6056350 DOI: 10.1002/jum.14537] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 08/23/2017] [Accepted: 10/22/2017] [Indexed: 05/11/2023]
Abstract
OBJECTIVES To assess the repeatability and reproducibility of the ultrasonic attenuation coefficient (AC) and backscatter coefficient (BSC) measured in the livers of adults with known or suspected nonalcoholic fatty liver disease (NAFLD). METHODS The Institutional Review Board approved this Health Insurance Portability and Accountability Act-compliant prospective study; informed consent was obtained. Forty-one research participants with known or suspected NAFLD were recruited and underwent same-day ultrasound examinations of the right liver lobe with a clinical scanner by a clinical sonographer. Each participant underwent 2 scanning trials, with participant repositioning between trials. Two transducers were used in each trial. For each transducer, machine settings were optimized by the sonographer but then kept constant while 3 data acquisitions were obtained from the liver without participant repositioning and then from an external calibrated phantom. Raw RF echo data were recorded. The AC and BSC were measured within 2.6 to 3.0 MHz from a user-defined hepatic field of interest from each acquisition. The repeatability and reproducibility were analyzed by random-effects models. RESULTS The mean AC and log-transformed BSC (logBSC) were 0.94 dB/cm-MHz and -27.0 dB, respectively. Intraclass correlation coefficients were 0.88 to 0.94 for the AC and 0.87 to 0.95 for the logBSC acquired without participant repositioning. For between-trial repeated scans with participant repositioning, the intraclass correlation coefficients were 0.80 to 0.84 for the AC and 0.69 to 0.82 for the logBSC after averaging results from 3 within-trial images. The variability introduced by the transducer was less than the repeatability error. CONCLUSIONS Hepatic AC and BSC measures using a reference phantom technique on a clinical scanner are repeatable and reproducible between transducers in adults with known or suspected NAFLD.
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Affiliation(s)
- Aiguo Han
- Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801
| | - Michael P. Andre
- Department of Radiology, University of California, San Diego, 9500 Gilman Dr., San Diego, CA 92093, and the San Diego VA Healthcare System, San Diego
| | - Lisa Deiranieh
- Department of Radiology, University of California, San Diego, 9500 Gilman Dr., San Diego, CA 92093, and the San Diego VA Healthcare System, San Diego
| | - Elise Housman
- Department of Radiology, University of California, San Diego, 9500 Gilman Dr., San Diego, CA 92093, and the San Diego VA Healthcare System, San Diego
| | - John W. Erdman
- Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, 905 South Goodwin Avenue, Urbana, IL 61801
| | - Rohit Loomba
- NAFLD Research Center, Division of Gastroenterology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093
| | - Claude B. Sirlin
- Liver Imaging Group, Department of Radiology, University of California, San Diego, 9452 Medical Center Drive, La Jolla, CA 92037
| | - William D. O’Brien
- Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801
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Klingensmith JD, Haggard A, Fedewa RJ, Qiang B, Cummings K, DeGrande S, Vince DG, Elsharkawy H. Spectral Analysis of Ultrasound Radiofrequency Backscatter for the Detection of Intercostal Blood Vessels. ULTRASOUND IN MEDICINE & BIOLOGY 2018; 44:1411-1422. [PMID: 29681422 DOI: 10.1016/j.ultrasmedbio.2018.03.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 02/23/2018] [Accepted: 03/12/2018] [Indexed: 06/08/2023]
Abstract
Spectral analysis of ultrasound radiofrequency backscatter has the potential to identify intercostal blood vessels during ultrasound-guided placement of paravertebral nerve blocks and intercostal nerve blocks. Autoregressive models were used for spectral estimation, and bandwidth, autoregressive order and region-of-interest size were evaluated. Eight spectral parameters were calculated and used to create random forests. An autoregressive order of 10, bandwidth of 6 dB and region-of-interest size of 1.0 mm resulted in the minimum out-of-bag error. An additional random forest, using these chosen values, was created from 70% of the data and evaluated independently from the remaining 30% of data. The random forest achieved a predictive accuracy of 92% and Youden's index of 0.85. These results suggest that spectral analysis of ultrasound radiofrequency backscatter has the potential to identify intercostal blood vessels. (jokling@siue.edu) © 2018 World Federation for Ultrasound in Medicine and Biology.
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Affiliation(s)
- Jon D Klingensmith
- Department of Electrical and Computer Engineering, Southern Illinois University Edwardsville, Edwardsville, Illinois, USA.
| | - Asher Haggard
- Department of Electrical and Computer Engineering, Southern Illinois University Edwardsville, Edwardsville, Illinois, USA
| | - Russell J Fedewa
- Department of Biomedical Engineering, The Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Beidi Qiang
- Department of Mathematics and Statistics, Southern Illinois University Edwardsville, Edwardsville, Illinois, USA
| | - Kenneth Cummings
- Anesthesiology Institute, The Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Sean DeGrande
- Anesthesiology Institute, The Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - D Geoffrey Vince
- Department of Biomedical Engineering, The Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Hesham Elsharkawy
- Anesthesiology Institute, The Cleveland Clinic Foundation, Cleveland, Ohio, USA
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Han A. A Method for Stereological Determination of the Structure Function From Histological Sections of Isotropic Scattering Media. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2018; 65:1007-1016. [PMID: 29856718 PMCID: PMC5997396 DOI: 10.1109/tuffc.2018.2818071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The frequency-dependent ultrasonic backscatter coefficient (BSC) from tissues, a fundamental parameter estimated by quantitative ultrasound (QUS) techniques, contains microstructure information useful for tissue characterization. To extract the microstructure information from the BSC, the tissue under investigation is often modeled as a collection of discrete scatterers embedded in a homogeneous background. From a discrete scatterer point of view, the BSC is dependent on not only the properties of individual scatterers relative to the background but also the scatterer spatial arrangement [described by the structure function (SF)]. Recently, the 2-D SF was computed from histological tissue sections, and was shown to be related to the volumetric SF extracted from QUS measurements. In this paper, a stereological method is proposed to extract the volumetric (3-D) SF from 2-D histological tissue sections. Simulations and experimental cell pellet biophantom studies were conducted to evaluate the proposed method. Simulation results verified the proposed method. Experimental results showed that the volumetric SF extracted using the proposed method had a significantly better agreement with the QUS-extracted SF than did the 2-D SF extracted in the previous study. The proposed stereological approach provides a useful tool for predicting the SF from histology.
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Muleki-Seya P, Han A, Andre MP, Erdman JW, O’Brien WD. Analysis of Two Quantitative Ultrasound Approaches. ULTRASONIC IMAGING 2018; 40:84-96. [PMID: 28945169 PMCID: PMC5780250 DOI: 10.1177/0161734617729159] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
There are two well-known ultrasonic approaches to extract sets of quantitative parameters: Lizzi-Feleppa (LF) parameters: slope, intercept, and midband; and quantitative ultrasound (QUS)-derived parameters: effective scatterer diameter (ESD) and effective acoustic concentration (EAC). In this study, the relation between the LF and QUS-derived parameters is studied theoretically and experimentally on ex vivo mouse livers. As expected from the theory, LF slope is correlated to ESD ([Formula: see text]), and from experimental data, LF midband is correlated to EAC ([Formula: see text]). However, LF intercept is not correlated to ESD ([Formula: see text]) nor EAC ([Formula: see text]). The unexpected correlation observed between LF slope and EAC ([Formula: see text]) results likely from the high correlation between ESD and EAC due to the inversion process. For a liver fat percentage estimation, an important potential medical application, the parameters presenting the better correlation are EAC ([Formula: see text]) and LF midband ([Formula: see text]).
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Affiliation(s)
- P. Muleki-Seya
- Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering, University of Illinois, 405 N. Mathews, Urbana, IL 61801
| | - A. Han
- Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering, University of Illinois, 405 N. Mathews, Urbana, IL 61801
| | - M. P. Andre
- Department of Radiology, University of California at San Diego, 9500 Gilman Drive, San Diego, CA 92093
| | - J. W. Erdman
- Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign, 905 S. Goodwin, Urbana, IL. 61801
| | - W. D. O’Brien
- Bioacoustics Research Laboratory, Department of Electrical and Computer Engineering, University of Illinois, 405 N. Mathews, Urbana, IL 61801
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Ramírez-Galván YA, Cardona-Huerta S, Elizondo-Riojas G, Álvarez-Villalobos NA. Apparent Diffusion Coefficient Value to Evaluate Tumor Response After Neoadjuvant Chemotherapy in Patients with Breast Cancer. Acad Radiol 2018; 25:179-187. [PMID: 29033147 DOI: 10.1016/j.acra.2017.08.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 08/10/2017] [Accepted: 08/28/2017] [Indexed: 12/11/2022]
Abstract
RATIONALE AND OBJECTIVES This study explored tumor behavior in patients with breast cancer during neoadjuvant chemotherapy (NAC) by sequential measurements of tumor apparent diffusion coefficient (ADC) after each chemotherapy cycle. The aim was to determine if the tumor ADC is useful to differentiate complete pathological response (cPR) from partial pathological response (pPR) during NAC. MATERIALS AND METHODS A total of 16 cases (in 14 patients) with diagnosis of breast cancer eligible to receive NAC were included. There were 70 magnetic resonance imaging examinations performed, 5 for each patient, during NAC cycles. Diffusion-weighted imaging was performed on a 1.5T system (b values of 0 and 700s/mm2). Four ADC ratios between the five MRI examinations were obtained to assess ADC changes during NAC. Absence of invasive breast cancer at surgical specimens (Miller-Payne 5) was considered as cPR and was used as reference for ADC cutoff ratios. RESULTS In this study, we were able to differentiate between cPR and pPR, after two cycles of NAC until the end of NAC before surgery (ADC ratios 2-4). The thresholds to differentiate between cPR and pPR of ADC ratios 2, 3, and 4, were 1.14 × 10-3mm2/s, 1.08 × 10-3mm2/s, and 1.25 × 10-3mm2/s, respectively, and have a cross-validated sensitivity and specificity of 79.2%, 79.7% (ADC ratio 2); 100%, 66.7% (ADC ratio 3); and 100%, 83.8% (ADC ratio 4), respectively. CONCLUSIONS The ADC ratios were useful to differentiate cPR from pPR in breast cancer tumors after NAC. Thus, it may be useful in tailoring treatment in these patients.
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Sannachi L, Gangeh M, Tadayyon H, Sadeghi-Naini A, Gandhi S, Wright FC, Slodkowska E, Curpen B, Tran W, Czarnota GJ. Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features. PLoS One 2018; 13:e0189634. [PMID: 29298305 PMCID: PMC5751990 DOI: 10.1371/journal.pone.0189634] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 11/28/2017] [Indexed: 12/31/2022] Open
Abstract
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.
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Affiliation(s)
- Lakshmanan Sannachi
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mehrdad Gangeh
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Hadi Tadayyon
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Ali Sadeghi-Naini
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Sonal Gandhi
- Division of Medical Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Frances C. Wright
- Division of General Surgery, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Elzbieta Slodkowska
- Department of Anatomic Pathology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Belinda Curpen
- Division of Breast Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - William Tran
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Gregory J. Czarnota
- Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
- Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- * E-mail:
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El Kaffas A, Gangeh MJ, Farhat G, Tran WT, Hashim A, Giles A, Czarnota GJ. Tumour Vascular Shutdown and Cell Death Following Ultrasound-Microbubble Enhanced Radiation Therapy. Am J Cancer Res 2018; 8:314-327. [PMID: 29290810 PMCID: PMC5743550 DOI: 10.7150/thno.19010] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2017] [Accepted: 08/11/2017] [Indexed: 12/13/2022] Open
Abstract
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.
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