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Anthropomorphic Polydimethylsiloxane silicone-based phantom for Diffuse Optical Imaging. Heliyon 2022; 8:e10308. [PMID: 36033332 PMCID: PMC9404336 DOI: 10.1016/j.heliyon.2022.e10308] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 07/06/2022] [Accepted: 08/11/2022] [Indexed: 11/24/2022] Open
Abstract
This work presents a method for constructing phantoms suitable for diffuse optical mammography. They are based on Polydimethylsiloxane silicones, with the characteristic of being anthropomorphic, and having similar mechanical and optical properties as a real breast. These phantoms are useful for testing the performance of diffuse optical imaging devices in the near infrared, both in transmittance and reflectance geometries, since they can be constructed containing inclusions, to simulate breast tumors. An alternative component to be used as scattering agent, that is easier to handle than traditional scattering agents, is also studied. The optical properties of the phantoms were tested varying the concentration of scattering and absorbing agents, while their mechanical properties were modified by adding a silicone fluid to the basic mixture. Finally, the phantoms were tested by Diffuse Optical Imaging experiments, and these images were compared to the ones obtained by conventional ultrasound techniques. Results show that the constructed anthropomorphic phantoms properly reproduce the optical and mechanical characteristics of human breasts, and are suitable to be used in Diffuse Optical Imaging. We constructed anthropomorphic phantoms for Diffuse Optical Imaging. They simulate the optical and mechanical characteristics of a human breast. A new scattering agent was successfully introduced. Results of Diffuse Optical images are compared to Ultrasound images.
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Cui H, Zhao D, Han P, Zhang X, Fan W, Zuo X, Wang P, Hu N, Kong H, Peng F, Wang Y, Tian J, Zhang L. Predicting Pathological Complete Response After Neoadjuvant Chemotherapy in Advanced Breast Cancer by Ultrasound and Clinicopathological Features Using a Nomogram. Front Oncol 2021; 11:718531. [PMID: 34888231 PMCID: PMC8650158 DOI: 10.3389/fonc.2021.718531] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 10/20/2021] [Indexed: 12/26/2022] Open
Abstract
Background and Aims Prediction of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer is critical for surgical planning and evaluation of NAC efficacy. The purpose of this project was to assess the efficiency of a novel nomogram based on ultrasound and clinicopathological features for predicting pCR after NAC. Methods This retrospective study included 282 patients with advanced breast cancer treated with NAC from two centers. Patients received breast ultrasound before NAC and after two cycles of NAC; and the ultrasound, clinicopathological features and feature changes after two cycles of NAC were recorded. A multivariate logistic regression model was combined with bootstrapping screened for informative features associated with pCR. Then, we constructed two nomograms: an initial-baseline nomogram and a two-cycle response nomogram. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) were analyzed. The C-index was used to evaluate predictive accuracy. Results Sixty (60/282, 21.28%) patients achieved pCR. Triple-negative breast cancer (TNBC) and HER2-amplified types were more likely to obtain pCR. Size shrinkage, posterior acoustic pattern, and elasticity score were identified as independent factors by multivariate logistic regression. In the validation cohort, the two-cycle response nomogram showed better discrimination than the initial-baseline nomogram, with the C-index reaching 0.79. The sensitivity, specificity, and NPV of the two-cycle response nomogram were 0.77, 0.77, and 0.92, respectively. Conclusion The two-cycle response nomogram exhibited satisfactory efficiency, which means that the nomogram was a reliable method to predict pCR after NAC. Size shrinkage after two cycles of NAC was an important in dependent factor in predicting pCR.
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Affiliation(s)
- Hao Cui
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Dantong Zhao
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Peng Han
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xudong Zhang
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Wei Fan
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xiaoxuan Zuo
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Panting Wang
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Nana Hu
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Hanqing Kong
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Fuhui Peng
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ying Wang
- Department of General Surgery, The Second Affiliated Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jiawei Tian
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lei Zhang
- Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
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Robbins CM, Tabassum S, Baumhauer MF, Yang J, Antaki JF, Kainerstorfer JM. Two-layer spatial frequency domain imaging of compression-induced hemodynamic changes in breast tissue. JOURNAL OF BIOMEDICAL OPTICS 2021; 26:056005. [PMCID: PMC8145994 DOI: 10.1117/1.jbo.26.5.056005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 05/04/2021] [Indexed: 06/15/2023]
Abstract
Significance: Longitudinal tracking of hemodynamic changes in the breast has shown potential for neoadjuvant chemotherapy (NAC) outcome prediction. Spatial frequency domain imaging (SFDI) could be suitable for frequent monitoring of shallow breast tumors, but strong sensitivity to superficial absorbers presents a challenge. Aim: We investigated the efficacy of a two-layer SFDI inverse model that accounts for varying melanin concentration in the skin to improve discrimination of optical properties of deep tissue of the breast. Approach: Hemodynamic changes in response to localized breast compression were measured in 13 healthy volunteers using a handheld SFDI device. Epidermis optical thickness was determined based on spectral fitting of the model output and used to calculate subcutaneous optical properties. Results: Optical properties from a homogeneous model yielded physiologically unreasonable absorption and scattering coefficients for highly pigmented volunteers. The two-layer model compensated for the effect of melanin and yielded properties in the expected range for healthy breast. Extracted epidermal optical thickness was higher for higher Fitzpatrick types. Compression induced a decrease in total hemoglobin consistent with tissue blanching. Conclusions: The handheld SFDI device and two-layer model show potential for imaging hemodynamic responses that potentially could help predict efficacy of NAC in patients of varying skin tones.
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Affiliation(s)
- Constance M. Robbins
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
| | - Syeda Tabassum
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
| | - Molly F. Baumhauer
- Carnegie Mellon University, Department of Physics, Pittsburgh, Pennsylvania, United States
| | - Jason Yang
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
| | - James F. Antaki
- Cornell University, School of Biomedical Engineering, Ithaca, New York, United States
| | - Jana M. Kainerstorfer
- Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, Pennsylvania, United States
- Carnegie Mellon University, Neuroscience Institute, Pittsburgh, Pennsylvania, United States
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Cochran JM, Busch DR, Leproux A, Zhang Z, O’Sullivan TD, Cerussi AE, Carpenter PM, Mehta RS, Roblyer D, Yang W, Paulsen KD, Pogue B, Jiang S, Kaufman PA, Chung SH, Schnall M, Snyder BS, Hylton N, Carp SA, Isakoff SJ, Mankoff D, Tromberg BJ, Yodh AG. Tissue oxygen saturation predicts response to breast cancer neoadjuvant chemotherapy within 10 days of treatment. JOURNAL OF BIOMEDICAL OPTICS 2018; 24:1-11. [PMID: 30338678 PMCID: PMC6194199 DOI: 10.1117/1.jbo.24.2.021202] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 08/30/2018] [Indexed: 05/20/2023]
Abstract
Ideally, neoadjuvant chemotherapy (NAC) assessment should predict pathologic complete response (pCR), a surrogate clinical endpoint for 5-year survival, as early as possible during typical 3- to 6-month breast cancer treatments. We introduce and demonstrate an approach for predicting pCR within 10 days of initiating NAC. The method uses a bedside diffuse optical spectroscopic imaging (DOSI) technology and logistic regression modeling. Tumor and normal tissue physiological properties were measured longitudinally throughout the course of NAC in 33 patients enrolled in the American College of Radiology Imaging Network multicenter breast cancer DOSI trial (ACRIN-6691). An image analysis scheme, employing z-score normalization to healthy tissue, produced models with robust predictions. Notably, logistic regression based on z-score normalization using only tissue oxygen saturation (StO2) measured within 10 days of the initial therapy dose was found to be a significant predictor of pCR (AUC = 0.92; 95% CI: 0.82 to 1). This observation suggests that patients who show rapid convergence of tumor tissue StO2 to surrounding tissue StO2 are more likely to achieve pCR. This early predictor of pCR occurs prior to reductions in tumor size and could enable dynamic feedback for optimization of chemotherapy strategies in breast cancer.
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Affiliation(s)
- Jeffrey M. Cochran
- University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania, United States
- Address all correspondence to: Jeffrey M. Cochran, E-mail:
| | - David R. Busch
- University of Texas Southwestern, Department of Anesthesiology and Pain Management, Dallas, Texas, United States
| | - Anaïs Leproux
- University of California, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
| | - Zheng Zhang
- Brown University School of Public Health, Department of Biostatistics and Center for Statistical Sciences, Providence, Rhode Island, United States
| | - Thomas D. O’Sullivan
- University of California, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
| | - Albert E. Cerussi
- University of California, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
| | - Philip M. Carpenter
- University of Southern California, Keck School of Medicine, Department of Pathology, Los Angeles, California, United States
| | - Rita S. Mehta
- University of California Irvine, Department of Medicine, Irvine, California, United States
| | - Darren Roblyer
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Wei Yang
- University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, Houston, Texas, United States
| | - Keith D. Paulsen
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, United States
| | - Brian Pogue
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, United States
| | - Shudong Jiang
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, United States
| | - Peter A. Kaufman
- Dartmouth-Hitchcock Medical Center, Department of Hematology and Oncology, Lebanon, New Hampshire, United States
| | - So Hyun Chung
- University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania, United States
| | - Mitchell Schnall
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Bradley S. Snyder
- Brown University School of Public Health, Center for Statistical Sciences, Providence, Rhode Island, United States
| | - Nola Hylton
- University of California, Department of Radiology, San Francisco, California, United States
| | - Stefan A. Carp
- Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Boston, Massachusetts, United States
| | - Steven J. Isakoff
- Massachusetts General Hospital, Department of Hematology and Oncology, Boston, Massachusetts, United States
| | - David Mankoff
- University of Pennsylvania, Division of Nuclear Medicine, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Bruce J. Tromberg
- University of California, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
| | - Arjun G. Yodh
- University of Pennsylvania, Department of Physics and Astronomy, Philadelphia, Pennsylvania, United States
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Vedantham S, Karellas A. Emerging Breast Imaging Technologies on the Horizon. Semin Ultrasound CT MR 2018; 39:114-121. [PMID: 29317033 DOI: 10.1053/j.sult.2017.09.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Early detection of breast cancers by mammography in conjunction with adjuvant therapy has contributed to reduction in breast cancer mortality. Mammography remains the "gold-standard" for breast cancer screening but is limited by tissue superposition. Digital breast tomosynthesis and more recently, dedicated breast computed tomography have been developed to alleviate the tissue superposition problem. However, all of these modalities rely upon x-ray attenuation contrast to provide anatomical images, and there are ongoing efforts to develop and clinically translate alternative modalities. These emerging modalities could provide for new contrast mechanisms and may potentially improve lesion detection and diagnosis. In this article, several of these emerging modalities are discussed with a focus on technologies that have advanced to the stage of in vivo clinical evaluation.
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Affiliation(s)
- Srinivasan Vedantham
- Department of Medical Imaging, University of Arizona College of Medicine, Banner University Medical Center, Tucson, AZ.
| | - Andrew Karellas
- Department of Medical Imaging, University of Arizona College of Medicine, Banner University Medical Center, Tucson, AZ
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