Multispectral Photoacoustic Analysis of Cancer: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Amina Yu and Version 1 by Jeesu Kim.

Photoacoustic imaging (PAI), one of the branches of optical imaging, provides the added advantage of increased imaging depth. Compared to other optical imaging techniques, PAI inherits ultrasound imaging characteristics (USI), which increases its ability to visualize structural information in deep tissue. The signal generation in PAI is based on the photoacoustic (PA) effect, which is energy transduction from light to ultrasound (US).

  • photoacoustic imaging
  • thyroid cancer

1. Introduction

In biomedical studiones, the characterization of molecular and functional information about the underlying tissue can significantly improve ourthe intuition in analyzing the morphology, treatment efficacy, and metabolites of target tissues. Among many biomedical imaging techniques, optical imaging methods have been widely applied for small animal studies d ones due to their cost-efficiency, ease of implementation, non-ionizing radiation, and real-time imaging capability [1]. More importantly, optical imaging techniques can also provide molecular and functional information by tuning the wavelength of the light source. While advantageous, the strong optical diffusion of the pure optical imaging techniques in biological tissues leads to a reduced penetration depth, thus limiting clinical translation.
Photoacoustic imaging (PAI), one of the branches of optical imaging, provides the added advantage of increased imaging depth [2]. Compared to other optical imaging techniques, PAI inherits ultrasound imaging characteristics (USI), which increases its ability to visualize structural information in deep tissue. The signal generation in PAI is based on the photoacoustic (PA) effect, which is energy transduction from light to ultrasound (US) [3]. In brief, PA images can be achieved through the following procedure: (i) pulse laser illumination, (ii) light absorption by chromophores, (iii) momentary heat generation, (iv) acoustic wave (i.for example., PA wave) generation through thermoelastic expansion, (v) signal detection by US transducer, and (vi) image generation. The resulting PA images, formed from the acoustic wave, include the optical absorption characteristics of the underlying biological tissue. Thus, PA images can provide molecular and functional information with a good ultrasound resolution in deep tissue [4,5][4][5]. In addition to the endogenous chromophores, contrast-enhanced PAI [6,7][6][7] has been focustudied by developing exogenous contrast agents including organic dyes [8,9,10,11,12][8][9][10][11][12] and inorganic nanoparticles [13,14,15,16,17,18][13][14][15][16][17][18]. Recently, contrast agents that absorb light in the second near-infrared region (NIR-II, 1000–1350 nm) have been investigated. In NIR-II, a greater imaging depth can be achieved with reduced tissue scattering and background noise compared to the first near-infrared region (NIR-I, 650–950 nm), which is mainly used for contrast-enhanced PAI [19,20,21,22,23,24,25][19][20][21][22][23][24][25].
One unique advantage of PAI is its scalable resolution and large imaging depth for the target region [26]. Since laser excitation can be tightly focused in shallow areas, high-resolution PA images can be achieved within the optical diffusion limit (~1 mm under the skin) [27,28][27][28]. High-resolution PAI has been used for visualizing the hemodynamics of the brain, ear, and eye of mice in vivo [29,30][29][30]. Since the light is diffused beyond the optical diffusion limit for deeper imaging depths, the US transducers determine the resolution at a greater depth [31,32,33,34][31][32][33][34].
For clinical ronesearch, PAI platforms have been developed and applied. Among them, the Vintage series (Verasonics, Kirkland, WA, USA), which equips the most-advanced programmable platform for designing user-defined operation sequence, is widely used [35,36][35][36]. The VevoLAZR series (FujiFilm VisualSonics, Toronto, ON, Canada) is another widespread commercial system for PAI research [37,38][37][38]. The main advantage of this system is a user-friendly interface, with real-time imaging and spectral analysis capability. Its high-frequency US transducer can provide high-resolution images, but it also limits the application area to a shallow area which is not favorable for clinical translation. The MSOT Acuity series (iThera Medical, Munich, Germany) has also been applied in clinical trials with multispectral PA analyses [39,40][39][40]. Its arc-shaped array can provide volumetric images, but its relatively small field of view is not suitable for general clinical applications that require a large imaging area. An FDA-cleared US machine (EC-12R, Alpinion Medical Systems, Anyang, Korea) has also been used to develop a clinical PAI system [41,42][41][42]. From the programmable platform in the US machine, the user can design their own operation sequence for their specific application.
Various geometry of multi-element array transducers has been developed and used for the clinical translation of PAI [43,44,45,46,47][43][44][45][46][47]. In typical clinical PAI systems, both US and PA images are acquired by controlling the data acquisition sequence [48]. The dual-modal images complement each other by visualizing molecular and functional information in the PAI and specific morphologies in the USI [49]. Dual-modal PA and US imaging (PAUSI) has been used for clinical investigation in humans [50,51][50][51]. The multispectral PA responses provide metabolomic information about the biological tissues, thus indirectly providing valuable information about the cancerous tissues [52,53][52][53].

2. Multispectral Photoacoustic Analysis of Thyroid Gland

Thyroid cancer is one of the most common cancers, with an increasing global incidence rate in men and women [63,64,65][54][55][56]. The gold standard for diagnosing thyroid nodules is fine-needle aspiration biopsy (FNAB) [66][57]. The triaging for FNAB of the nodule is determined by the characteristics of nodules in USI [67,68,69][58][59][60]. Although the sensitivity of US-guided triaging is greater than 90%, the lack of functional metabolomics results in a low specificity of 20–50% [70][61]. The high false-positive rate leads to unnecessary FNAB, which results in the over-diagnosing of the tumor. Thus, clinical trials have been conducted to enhance the accuracy of triaging thyroid nodules using PAI due to its molecular and functional imaging capability. Dogra et al. analyzed 88 resected tissues (13 malignant nodules, 30 benign nodules, 13 colloid accumulations, and 32 normal tissues) from 50 patients (11 malignant and 39 benign) [71][62]. Four different wavelengths (760, 850, 930, and 970 nm) were used for the spectral unmixing of HbO, HbR, lipid, and water components from multispectral PA data. Statistically significant differences were found in HbO and HbR between malignant and other types of tissues. In particular, HbR components were significantly different between malignant and normal tissue, with a p-value of 0.003 in the student t-test. The results showed the promising feasibility of PA-guided classification with a sensitivity of 69.2% and a specificity of 96.9%, but this study was limited to ex vivo environments only. Thus, for clinical translation, further in vivo validation is needed. Dima et al. demonstrated the in vivo imaging capability of their PA and US system for the human thyroid [72][63]. They recruited two healthy volunteers to acquire PA images with a single excitation wavelength of 800 nm. US Doppler images were also acquired in the same region to verify the blood vessel’s position. By comparing the PA images with the US Doppler images, surrounding blood vessels extending from the isthmus and carotid artery to the anterior of the thyroid gland were identified. The results showed the feasibility of in vivo PAI using the arc array US transducer by confirming the matched positions of blood vessels (white arrows in. However, the spectral analysis of cancerous nodules was not available in this study. Yang et al. compared in vivo PA images between papillary thyroid cancer (PTC) patients and healthy volunteers [73][64]. Although they achieved PA responses from cancerous nodules, the number of patients (10 PTC and 3 normal) included in the study was insufficient for statistical analysis. In addition, multispectral analysis was also not available in this study because they used a single excitation wavelength of 1064 nm. Roll et al. presented multispectral PA analyses for differentiating tissue disorders in the thyroid gland [74][65]. The composition of HbO, HbR, fat, and water were spectrally unmixed from the in vivo PA images of the enrolled patients (6 Graves’ disease, 3 malignant, 13 benign, and 8 healthy), obtained using eight excitation wavelengths (700, 730, 760, 800, 850, 900, 920, and 950 nm). The sO2 levels of the thyroid were also visualized and investigated. The contours of the thyroid glands were determined by the corresponding US images. Statistical analyses demonstrated significant differences between diseased and normal thyroid tissues. Recently, Kim et al. presented a multispectral PA analysis with a statistically sufficient number of samples (23 PTC and 29 benign), the largest number of patients in a clinical thyroid studyone to date [75][66]. They achieved multispectral PAI using five wavelengths (700, 756, 796, 866, and 900 nm). The corresponding US data were also acquired simultaneously for delineating the boundary of nodules. Similar to the previous studiones, the sO2 levels in nodules were acquired through the spectral unmixing of HbO and HbR. Three parameters were quantified and used for training the decision function: (i) PA spectral gradient: the slope of a first-order polynomial fitted line to the average value of the top 50% of PA signals within the nodule boundary at each wavelength; (ii) relative sO2 level: the average value of the top 50% sO2 values within the nodule; (iii) skewed angle of sO2 distribution: the skewed angle of the Gaussian-fitted distribution for the top 50% of sO2 values within the nodule. With the values of the three parameters scattered in a 3D plane, a support vector machine was trained to determine the 3D decision boundary, which showed a good classification accuracy with a sensitivity of 78% and a specificity of 93%. The classification accuracy was further enhanced using a novel scoring method (ATAP score), which combined a conventional USI-based scoring method (i.for example., ATA guideline score) and the photoacoustic probability of malignancy. The novel scoring method improved the sensitivity to 83% and the specificity to 93%. Thus, the results showed a great potential for enhancing the triaging accuracy of thyroid nodules using a multiparametric analysis of multispectral PA data as a complementary method to the conventional triaging method. While PA analyses of thyroid nodules have been conducted by various groups worldwide, the validation of multispectral PA analysis is still at the initial stage of evaluation. Further situdies are was required to address the following issues for successful clinical translation. (i) Larger number of patients are needed to enhance the reliability of this technique. (ii) In addition to PTC, the classification of other types of thyroid cancers such as follicular, medullary, and anaplastic thyroid cancers would expand the application area. (iii) Quantitative analyses of PA responses in skin color are needed. (iv) System improvement with a deeper imaging depth, faster frame rate, and smaller size would enhance the image quality for multispectral analyses.

References

  1. Luker, G.D.; Luker, K.E. Optical Imaging: Current Applications and Future Directions. J. Nucl. Med. 2008, 49, 1–4.
  2. Kim, C.; Favazza, C.; Wang, L.V. In Vivo Photoacoustic Tomography of Chemicals: High-Resolution Functional and Molecular Optical Imaging at New Depths. Chem. Rev. 2010, 110, 2756–2782.
  3. Bell, A.G. The Photophone. Science 1880, 1, 130–134.
  4. Baik, J.W.; Kim, H.; Son, M.; Choi, J.; Kim, K.G.; Baek, J.H.; Park, Y.H.; An, J.; Choi, H.Y.; Ryu, S.Y.; et al. Intraoperative Label-Free Photoacoustic Histopathology of Clinical Specimens. Laser Photonics Rev. 2021, 15, 2100124.
  5. Ahn, J.; Kim, J.Y.; Choi, W.; Kim, C. High-Resolution Functional Photoacoustic Monitoring of Vascular Dynamics in Human Fingers. Photoacoustics 2021, 23, 100282.
  6. Upputuri, P.K.; Pramanik, M. Recent Advances in Photoacoustic Contrast Agents for In Vivo Imaging. Wires. Nanomed. Nanobiotechnol. 2020, 12, e1618.
  7. Park, B.; Park, S.; Kim, J.; Kim, C. Listening to Drug Delivery and Responses via Photoacoustic Imaging. Adv. Drug Deliv. Rev. 2022, 184, 114235.
  8. Kim, J.; Park, S.; Lee, C.; Kim, J.Y.; Kim, C. Organic Nanostructures for Photoacoustic Imaging. ChemNanoMat 2015, 2, 156–166.
  9. Wang, X.; Ku, G.; Wegiel, M.A.; Bornhop, D.J.; Stoica, G.; Wang, L.V. Noninvasive Photoacoustic Angiography of Animal Brains In Vivo with Near-Infrared Light and an Optical Contrast Agent. Opt. Lett. 2004, 29, 730–732.
  10. Ku, G.; Wang, L.V. Deeply Penetrating Photoacoustic Tomography in Biological Tissues Enhanced with an Optical Contrast Agent. Opt. Lett. 2005, 30, 507–509.
  11. Cheng, H.-B.; Li, Y.; Tang, B.Z.; Yoon, J. Assembly Strategies of Organic-Based Imaging Agents for Fluorescence and Photoacoustic Bioimaging Applications. Chem. Soc. Rev. 2020, 49, 21–31.
  12. Zha, Z.; Deng, Z.; Li, Y.; Li, C.; Wang, J.; Wang, S.; Qu, E.; Dai, Z. Biocompatible Polypyrrole Nanoparticles as a Novel Organic Photoacoustic Contrast Agent for Deep Tissue Imaging. Nanoscale 2013, 5, 4462–4467.
  13. Jeong, W.Y.; Kang, M.S.; Lee, H.; Lee, J.H.; Kim, J.; Han, D.-W.; Kim, K.S. Recent Trends in Photoacoustic Imaging Techniques for 2D Nanomaterial-Based Phototherapy. Biomedicines 2021, 9, 80.
  14. Zhang, Q.; Iwakuma, N.; Sharma, P.; Moudgil, B.; Wu, C.; McNeill, J.; Jiang, H.; Grobmyer, S. Gold Nanoparticles as a Contrast Agent for In Vivo Tumor Imaging with Photoacoustic Tomography. Nanotechnology 2009, 20, 395102.
  15. Yang, H.-W.; Liu, H.-L.; Li, M.-L.; Hsi, I.-W.; Fan, C.-T.; Huang, C.-Y.; Lu, Y.-J.; Hua, M.-Y.; Chou, H.-Y.; Liaw, J.-W. Magnetic Gold-Nanorod/PNIPAAmMA Nanoparticles for Dual Magnetic Resonance and Photoacoustic Imaging and Targeted Photothermal Therapy. Biomaterials 2013, 34, 5651–5660.
  16. Li, W.; Chen, X. Gold Nanoparticles for Photoacoustic Imaging. Nanomedicine 2015, 10, 299–320.
  17. Xie, H.; Liu, M.; You, B.; Luo, G.; Chen, Y.; Liu, B.; Jiang, Z.; Chu, P.K.; Shao, J.; Yu, X.F. Biodegradable Bi2O2Se Quantum Dots for Photoacoustic Imaging-Guided Cancer Photothermal Therapy. Small 2020, 16, 1905208.
  18. Guo, T.; Tang, Q.; Guo, Y.; Qiu, H.; Dai, J.; Xing, C.; Zhuang, S.; Huang, G. Boron Quantum Dots for Photoacoustic Imaging-Guided Photothermal Therapy. Acs Appl. Mater. Interfaces 2020, 13, 306–311.
  19. Chitgupi, U.; Nyayapathi, N.; Kim, J.; Wang, D.; Sun, B.; Li, C.; Carter, K.; Huang, W.C.; Kim, C.; Xia, J. Surfactant-Stripped Micelles for NIR-II Photoacoustic Imaging through 12 cm of Breast Tissue and Whole Human Breasts. Adv. Mater. 2019, 31, 1902279.
  20. Park, B.; Lee, K.M.; Park, S.; Yun, M.; Choi, H.-J.; Kim, J.; Lee, C.; Kim, H.; Kim, C. Deep Tissue Photoacoustic Imaging of Nickel (II) Dithiolene-Containing Polymeric Nanoparticles in the Second Near-Infrared Window. Theranostics 2020, 10, 2509–2521.
  21. Park, S.; Park, G.; Kim, J.; Choi, W.; Jeong, U.; Kim, C. Bi2Se3 Nanoplates for Contrast-Enhanced Photoacoustic Imaging at 1064 nm. Nanoscale 2018, 10, 20548–20558.
  22. Jiang, Y.; Upputuri, P.K.; Xie, C.; Lyu, Y.; Zhang, L.; Xiong, Q.; Pramanik, M.; Pu, K. Broadband Absorbing Semiconducting Polymer Nanoparticles for Photoacoustic Imaging in Second Near-Infrared Window. Nano Lett. 2017, 17, 4964–4969.
  23. Wu, J.; You, L.; Lan, L.; Lee, H.J.; Chaudhry, S.T.; Li, R.; Cheng, J.X.; Mei, J. Semiconducting Polymer Nanoparticles for Centimeters-Deep Photoacoustic Imaging in the Second Near-Infrared Window. Adv. Mater. 2017, 29, 1703403.
  24. Jiang, Y.; Pu, K. Molecular Fluorescence and Photoacoustic Imaging in the Second Near-Infrared Optical Window using Organic Contrast Agents. Adv. Biosyst. 2018, 2, 1700262.
  25. Lyu, Y.; Li, J.; Pu, K. Second Near-Infrared Absorbing Agents for Photoacoustic Imaging and Photothermal Therapy. Small Methods 2019, 3, 1900553.
  26. Wang, L.V.; Hu, S. Photoacoustic Tomography: In Vivo Imaging From Organelles to Organs. Science 2012, 335, 1458–1462.
  27. Yao, J.; Wang, L.V. Photoacoustic Microscopy. Laser Photonics Rev. 2013, 7, 758–778.
  28. Cho, S.-W.; Park, S.M.; Park, B.; Lee, T.G.; Kim, B.-M.; Kim, C.; Kim, J.; Lee, S.-W.; Kim, C.-S. High-Speed Photoacoustic Microscopy: A Review Dedicated on Light Sources. Photoacoustics 2021, 24, 100291.
  29. Kim, J.Y.; Lee, C.; Park, K.; Lim, G.; Kim, C. Fast Optical-Resolution Photoacoustic Microscopy using a 2-Axis Water-Proofing MEMS Scanner. Sci. Rep. 2015, 5, 7932.
  30. Nasiriavanaki, M.; Xia, J.; Wan, H.; Bauer, A.Q.; Culver, J.P.; Wang, L.V. High-Resolution Photoacoustic Tomography of Resting-State Functional Connectivity in the Mouse Brain. Proc. Natl. Acad. Sci. USA 2014, 111, 21–26.
  31. Jeon, M.; Kim, J.; Kim, C. Multiplane Spectroscopic Whole-Body Photoacoustic Imaging of Small Animals In Vivo. Med. Biol. Eng. Comput. 2016, 54, 283–294.
  32. Li, L.; Zhu, L.; Ma, C.; Lin, L.; Yao, J.; Wang, L.; Maslov, K.; Zhang, R.; Chen, W.; Shi, J. Single-Impulse Panoramic Photoacoustic Computed Tomography of Small-Animal Whole-Body Dynamics at High Spatiotemporal Resolution. Nat. Biomed. 2017, 1, 1–11.
  33. Ermilov, S.; Su, R.; Conjusteau, A.; Anis, F.; Nadvoretskiy, V.; Anastasio, M.; Oraevsky, A. Three-Dimensional Optoacoustic and Laser-Induced Ultrasound Tomography System for Preclinical Research in Mice: Design and Phantom Validation. Ultrasonic Imaging 2016, 38, 77–95.
  34. Park, J.; Park, B.; Kim, T.; Jung, S.; Choi, W.; Ahn, J.; Yoon, D.; Kim, J.; Jeon, S.; Lee, D. Quadruple fusion imaging via transparent ultrasound transducer: Ultrasound, photoacoustic, optical coherence, and fluorescence imaging. Proc. Natl. Acad. Sci. USA 2021, 118, e1920879118.
  35. Yuan, J.; Xu, G.; Yu, Y.; Zhou, Y.; Carson, P.L.; Wang, X.; Liu, X. Real-Time Photoacoustic and Ultrasound Dual-Modality Imaging System Facilitated with Graphics Processing Unit and Code Parallel Optimization. J. Biomed. Opt. 2013, 18, 086001.
  36. Kothapalli, S.-R.; Sonn, G.A.; Choe, J.W.; Nikoozadeh, A.; Bhuyan, A.; Park, K.K.; Cristman, P.; Fan, R.; Moini, A.; Lee, B.C.; et al. Simultaneous Transrectal Ultrasound and Photoacoustic Human Prostate Imaging. Sci. Transl. Med. 2019, 11, 1–12.
  37. Needles, A.; Heinmiller, A.; Sun, J.; Theodoropoulos, C.; Bates, D.; Hirson, D.; Yin, M.; Foster, F.S. Development and initial application of a fully integrated photoacoustic micro-ultrasound system. IEEE T. Ultrason. Ferr. 2013, 60, 888–897.
  38. Zafar, H.; Breathnach, A.; Subhash, H.M.; Leahy, M.J. Linear-Array-Based Photoacoustic Imaging of Human Microcirculation with a Range of High Frequency Transducer Probes. J. Biomed. Opt. 2015, 20, 051021.
  39. Levi, J.; Sathirachinda, A.; Gambhir, S.S. A High-Affinity, High-Stability Photoacoustic Agent for Imaging Gastrin-Releasing Peptide Receptor in Prostate Cancer. Clin. Cancer Res. 2014, 20, 3721–3729.
  40. Becker, A.; Masthoff, M.; Claussen, J.; Ford, S.J.; Roll, W.; Burg, M.; Barth, P.J.; Heindel, W.; Schaefers, M.; Eisenblaetter, M. Multispectral Optoacoustic Tomography of the Human Breast: Characterisation of Healthy Tissue and Malignant Lesions using a Hybrid Ultrasound-Optoacoustic Approach. Eur. Radiol. 2018, 28, 602–609.
  41. Kim, J.; Park, S.; Jung, Y.; Chang, S.; Park, J.; Zhang, Y.; Lovell, J.F.; Kim, C. Programmable Real-time Clinical Photoacoustic and Ultrasound Imaging System. Sci. Rep. 2016, 6, 35137.
  42. Kim, J.; Park, E.-Y.; Park, B.; Choi, W.; Lee, K.J.; Kim, C. Towards Clinical Photoacoustic and Ultrasound Imaging: Probe Improvement and Real-Time Graphical User Interface. Exp. Biol. Med. 2020, 245, 321–329.
  43. Luís Deán-Ben, X.; Razansky, D. Adding Fifth Dimension to Optoacoustic Imaging: Volumetric Time-Resolved Spectrally Enriched Tomography. Light-Sci. Appl. 2014, 3, e137.
  44. Yang, J.; Choi, S.; Kim, C. Practical Review on Photoacoustic Computed Tomography using Curved Ultrasound Array Transducer. Biomed. Eng. Lett. 2022, 12, 19–35.
  45. Gateau, J.; Caballero, M.Á.A.; Dima, A.; Ntziachristos, V. Three-Dimensional Optoacoustic Tomography using a Conventional Ultrasound Linear Detector Array: Whole-Body Tomographic System for Small Animals. Med. Phys. 2013, 40, 013302.
  46. Kim, C.; Erpelding, T.N.; Jankovic, L.; Wang, L.V. Performance Benchmarks of an Array-Based Hand-Held Photoacoustic Probe Adapted from a Clinical Ultrasound System for Non-Invasive Sentinel Lymph Node Imaging. Philos. Trans. R. Soc. A 2011, 369, 4644–4650.
  47. Lee, C.; Choi, W.; Kim, J.; Kim, C. Three-Dimensional Clinical Handheld Photoacoustic/Ultrasound Scanner. Photoacoustics 2020, 18, 100173.
  48. Jeon, S.; Choi, W.; Park, B.; Kim, C. A Deep Learning-Based Model that Reduces Speed of Sound Aberrations for Improved In Vivo Photoacoustic Imaging. IEEE T. Image Process 2021, 30, 8773–8784.
  49. Park, E.-Y.; Lee, H.; Han, S.; Kim, C.; Kim, J. Photoacoustic Imaging Systems Based on Clinical Ultrasound Platform. Exp. Biol. Med. 2022, 247, 551–560.
  50. Steinberg, I.; Huland, D.M.; Vermesh, O.; Frostig, H.E.; Tummers, W.S.; Gambhir, S.S. Photoacoustic Clinical Imaging. Photoacoustics 2019, 14, 77–98.
  51. Choi, W.; Park, E.-Y.; Jeon, S.; Kim, C. Clinical Photoacoustic Imaging Platforms. Biomed. Eng. Lett. 2018, 8, 139–155.
  52. Park, B.; Bang, C.H.; Lee, C.; Han, J.H.; Choi, W.; Kim, J.; Park, G.S.; Rhie, J.W.; Lee, J.H.; Kim, C. 3D Wide-Field Multispectral Photoacoustic Imaging of Human Melanomas In Vivo: A Pilot Study. J. Eur. Acad. Dermatol. 2020, 35, 669–676.
  53. Kim, J.; Kim, Y.H.; Park, B.; Seo, H.M.; Bang, C.H.; Park, G.S.; Park, Y.M.; Rhie, J.W.; Lee, J.H.; Kim, C. Multispectral Ex Vivo Photoacoustic Imaging of Cutaneous Melanoma for Better Selection of the Excision Margin. Br. J. Dermatol. 2018, 179, 780–782.
  54. Vaccarella, S.; Dal Maso, L.; Laversanne, M.; Bray, F.; Plummer, M.; Franceschi, S. The Impact of Diagnostic Changes on the Rise in Thyroid Cancer Incidence: A Population-Based Study in Selected High-Resource Countries. Thyroid 2015, 25, 1127–1136.
  55. Megwalu, U.; Moon, P.K. Thyroid Cancer Incidence and Mortality Trends in the United States: 2000–2018. Thyroid, 2022; in press.
  56. Ferlay, J.; Colombet, M.; Soerjomataram, I.; Mathers, C.; Parkin, D.; Piñeros, M.; Znaor, A.; Bray, F. Estimating the Global Cancer Incidence and Mortality in 2018: GLOBOCAN Sources and Methods. Int. J. Cancer 2019, 144, 1941–1953.
  57. Cooper, D.S.; Doherty, G.M.; Haugen, B.R.; Kloos, R.T.; Lee, S.L.; Mandel, S.J.; Mazzaferri, E.L.; McIver, B.; Sherman, S.I.; Tuttle, R.M. Management Guidelines for Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Taskforce. Thyroid 2006, 16, 109–142.
  58. Haugen, B.R.; Alexander, E.K.; Bible, K.C.; Doherty, G.M.; Mandel, S.J.; Nikiforov, Y.E.; Pacini, F.; Randolph, G.W.; Sawka, A.M.; Schlumberger, M. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid 2016, 26, 1–133.
  59. Perros, P.; Boelaert, K.; Colley, S.; Evans, C.; Evans, R.M.; Gerrard BA, G.; Gilbert, J.; Harrison, B.; Johnson, S.J.; Giles, T.E. Guidelines for the Management of Thyroid Cancer. Clin. Endocrinol. 2014, 81, 1–122.
  60. Kwak, J.Y.; Han, K.H.; Yoon, J.H.; Moon, H.J.; Son, E.J.; Park, S.H.; Jung, H.K.; Choi, J.S.; Kim, B.M.; Kim, E.-K. Thyroid Imaging Reporting and Data System for US Features of Nodules: A Step in Establishing Better Stratification of Cancer Risk. Radiology 2011, 260, 892–899.
  61. Chng, C.L.; Tan, H.C.; Too, C.W.; Lim, W.Y.; Chiam, P.P.S.; Zhu, L.; Nadkarni, N.V.; Lim, A.Y.Y. Diagnostic Performance of ATA, BTA and TIRADS Sonographic Patterns in the Prediction of Malignancy in Histologically Proven Thyroid Nodules. Singap. Med. J. 2018, 59, 578.
  62. Dogra, V.S.; Chinni, B.K.; Valluru, K.S.; Moalem, J.; Giampoli, E.J.; Evans, K.; Rao, N.A. Preliminary Results of Ex Vivo Multispectral Photoacoustic Imaging in the Management of Thyroid Cancer. Am. J. Roentgenol. 2014, 202, W552–W558.
  63. Dima, A.; Ntziachristos, V. In-Vivo Handheld Optoacoustic Tomography of the Human Thyroid. Photoacoustics 2016, 4, 65–69.
  64. Yang, M.; Zhao, L.; He, X.; Su, N.; Zhao, C.; Tang, H.; Hong, T.; Li, W.; Yang, F.; Lin, L. Photoacoustic/Ultrasound Dual Imaging of Human Thyroid Cancers: An Initial Clinical Study. Biomed. Opt. Express 2017, 8, 3449–3457.
  65. Roll, W.; Markwardt, N.A.; Masthoff, M.; Helfen, A.; Claussen, J.; Eisenblätter, M.; Hasenbach, A.; Hermann, S.; Karlas, A.; Wildgruber, M. Multispectral Optoacoustic Tomography of Benign and Malignant Thyroid Disorders—A Pilot Study. J. Nucl. Med. 2019, 60, 1461–1466.
  66. Kim, J.; Park, B.; Ha, J.; Steinberg, I.; Hooper, S.M.; Jeong, C.; Park, E.-Y.; Choi, W.; Liang, T.; Bae, J.-S. Multiparametric Photoacoustic Analysis of Human Thyroid Cancers In Vivo. Cancer Res. 2021, 81, 4849–4860.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback