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Savitzky-Golay Filter Based Quantitative Dynamic Contrast-Enhanced Ultrasound on Assessing Therapeutic Response in Mice with Hepatocellular Carcinoma

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Abstract

As new drugs are developed in targeted therapy for advanced hepatocellular carcinoma (HCC), an accurate evaluation procedure for the therapeutic efficacy is needed. Current methods use MRI or CT based response evaluation criteria in solid tumors (RECIST) which is unsatisfactory for overlooking the functional response. We propose a new mice model of HCC for assessment of the early response to targeted therapy with contrast-enhanced ultrasound (CEUS). The major technical innovation is analysis of tumor functional characteristics using Savitzky-Golay filter (S-G filter) based CEUS quantification (SGCQ) software. In this study, mice were divided into three groups, including the control group (n1 = 18), sorafenib treatment group (n2 = 18) and lenvatinib treatment group (n3 = 18). SGCQ software specialized in data smoothing was used to quantify the time, enhanced intensity and blood volume related parameters at five different time points within 14 days of therapy. Promising experimental results were obtained. From the analysis, it could detect response as early as 4th day and perfusion time (PT), mean transit time (MTT), area under the curve of tumor/adjacent parenchyma (qAUC), wash-in slope a3, the average time of perfusion (T0) were early predictors. Then, tumors were excised with histopathology performed, CD31 H-score is in correlation with parameters peak intensity (PI), enhanced intensity (EI) and area under the curve of tumor/adjacent parenchyma (qAUC). Moreover, there was no significant difference in efficacy between sorafenib and lenvatinib in both CEUS parameters and histopathology. Finally, the finding of this study proves SGCQ software to be a valid, sensitive and repeatable method for therapeutic evaluation. Quantitative and comparative studies show that sorafenib and lenvatinib, as two first-line targeted drugs, ensure the therapeutic advantages of HCC.

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References

  1. Llovet, J. M., Zucman-Rossi, J., Pikarsky, E., Sangro, B., Schwartz, M., Sherman, M., et al. (2016). Hepatocellular carcinoma. Nature Reviews. Disease Primers, 2, 16018.

    Article  Google Scholar 

  2. Kudo, M., Finn, R. S., Qin, S., Han, K. H., Ikeda, K., Piscaglia, F., et al. (2018). Lenvatinib versus sorafenib in first-line treatment of patients with unresectable hepatocellular carcinoma: A randomised phase 3 non-inferiority trial. Lancet., 10126(391), 1163–1173.

    Article  Google Scholar 

  3. Llovet, J. M., Ricci, S., Mazzaferro, V., Hilgard, P., Gane, E., Blanc, J. F., et al. (2008). Sorafenib in advanced hepatocellular carcinoma. The New England Journal of Medicine, 4(359), 378–390.

    Article  Google Scholar 

  4. Li, Y., Gao, Z. H., & Qu, X. J. (2015). The adverse effects of sorafenib in patients with advanced cancers. Basic & Clinical Pharmacology & Toxicology, 3(116), 216–221.

    Article  Google Scholar 

  5. Kantarci, M., & Pirimoglu, B. (2017). Radiological response to the Locoregional treatment in hepatocellular carcinoma: RECIST, mRECIST, and others. Journal of Gastrointestinal Cancer, 48(3), 282–285.

    Article  Google Scholar 

  6. Morse, D. L., & Gillies, R. J. (2010). Molecular imaging and targeted therapies. Biochemical Pharmacology, 5(80), 731–738.

    Article  Google Scholar 

  7. Choi, K. J., Baik, I. H., Ye, S. K., & Lee, Y. H. (2015). Molecular targeted therapy for hepatocellular carcinoma: Present status and future directions. Biological & Pharmaceutical Bulletin, 7(38), 986–991.

    Article  Google Scholar 

  8. Hanna, R. F., Miloushev, V. Z., Tang, A., Finklestone, L. A., Brejt, S. Z., Sandhu, R. S., et al. (2016). Comparative 13-year meta-analysis of the sensitivity and positive predictive value of ultrasound, CT, and MRI for detecting hepatocellular carcinoma. Abdominal Radiology (New York), 1(41), 71–90.

    Article  Google Scholar 

  9. Baron Toaldo, M., Salvatore, V., Marinelli, S., Palama, C., Milazzo, M., Croci, L., et al. (2015). Use of VEGFR-2 targeted ultrasound contrast agent for the early evaluation of response to sorafenib in a mouse model of hepatocellular carcinoma. Molecular Imaging and Biology, 1(17), 29–37.

    Article  Google Scholar 

  10. Tian, H., & Wang, Q. (2016). Quantitative analysis of microcirculation blood perfusion in patients with hepatocellular carcinoma before and after transcatheter arterial chemoembolisation using contrast-enhanced ultrasound. European Journal of Cancer, 68, 82–89.

    Article  Google Scholar 

  11. Xin, L., Yan, Z., Zhang, X., Zang, Y., Ding, Z., Xue, H., et al. (2017). Parameters for contrast-enhanced ultrasound (CEUS) of enlarged superficial lymph nodes for the evaluation of therapeutic response in lymphoma: A preliminary study. Medical Science Monitor, 23, 5430–5438.

    Article  Google Scholar 

  12. Knieling, F., Waldner, M. J., Goertz, R. S., Zopf, S., Wildner, D., Neurath, M. F., et al. (2013). Early response to anti-tumoral treatment in hepatocellular carcinoma--can quantitative contrast-enhanced ultrasound predict outcome? ULTRASCHALL MED., 1(34), 38–46.

    Google Scholar 

  13. Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 8(36), 1627–1639.

    Article  Google Scholar 

  14. Randhawa, S. K., & Sunkaria, R. K. (2018). Investigation of performance of Savitzky-Golay filter for speckle reduction in ultrasound images. In 2018 first international conference on secure cyber computing and communication (ICSCCC), Jalandhar, India. 2018-01-01.

  15. Peng, S., Ding, H., Fu, T., Wang, B., Wang, W., & Zhou, J. (2018). Savitzky-Golay filter based contrast-enhanced ultrasound quantification in hepatic tumors: Methodology and its correlation with tumor angiogenesis. Clinical Hemorheology and Microcirculation.

  16. Mori, T., Hamaya, Y., Uotani, T., Yamade, M., Iwaizumi, M., Furuta, T., et al. (2018). Prevalence of elevated microsatellite alterations at selected tetranucleotide repeats in pancreatic ductal adenocarcinoma. PLoS One, 12(13), e208557.

    Google Scholar 

  17. Jiang, H. Y., Chen, J., Xia, C. C., Cao, L. K., Duan, T., & Song, B. (2018). Noninvasive imaging of hepatocellular carcinoma: From diagnosis to prognosis. World Journal of Gastroenterology, 22(24), 2348–2362.

    Article  Google Scholar 

  18. Ferraioli, G., & Meloni, M. F. (2018). Contrast-enhanced ultrasonography of the liver using SonoVue. Ultrasonography., 1(37), 25–35.

    Article  Google Scholar 

  19. Yuan, Z., Quan, J., Yunxiao, Z., Jian, C., Zhu, H., & Liping, G. (2013). Diagnostic value of contrast-enhanced ultrasound parametric imaging in breast tumors. Journal of Breast Cancer, 2(16), 208–213.

    Article  Google Scholar 

  20. Lee, H. S., Cho, C. M., Kwon, Y. H., & Nam, S. Y. (2019). Predicting malignancy risk in gastrointestinal subepithelial tumors with contrast-enhanced harmonic endoscopic ultrasonography using perfusion analysis software. Gut and Liver, 2(13), 161–168.

    Article  Google Scholar 

  21. Zhan, Y., Zhou, F., Yu, X., Luo, F., Liu, F., Liang, P., et al. (2019). Quantitative dynamic contrast-enhanced ultrasound may help predict the outcome of hepatocellular carcinoma after microwave ablation. International Journal of Hyperthermia, 1(35), 105–111.

    Google Scholar 

  22. Frachon, S., Gouysse, G., Dumortier, J., Couvelard, A., Nejjari, M., Mion, F., et al. (2001). Endothelial cell marker expression in dysplastic lesions of the liver: An immunohistochemical study. Journal of Hepatology, 6(34), 850–857.

    Article  Google Scholar 

  23. Ueda, N., Nagira, H., Sannomiya, N., Ikunishi, S., Hattori, Y., Kamida, A., et al. (2016). Contrast-enhanced ultrasonography in evaluation of the therapeutic effect of chemotherapy for patients with liver metastases. Yonago Acta Medica, 4(59), 255–261.

    Google Scholar 

  24. Kuorda, H., Abe, T., Fujiwara, Y., Okamoto, T., Yonezawa, M., Sato, H., et al. (2019). Change in arterial tumor perfusion is an early biomarker of lenvatinib efficacy in patients with unresectable hepatocellular carcinoma. World Journal of Gastroenterology, 19(25), 2365–2372.

    Article  Google Scholar 

  25. Yu, J., Lin, F., Seah, H., Li, C., & Lin, Z. (2012). Image classification by multimodal subspace learning. Pattern Recognition Letters, 9(33), 1196–1204.

    Article  Google Scholar 

  26. Patricia, S. P., & Thong, S. S. T. M. (2012). Towards real-time virtual biopsy of oral lesions using confocal laser endomicroscopy interfaced with embedded computing. Journal of Biomedical Optics, 5(17).

  27. Wang, T., Jiang, Z., Kemao, Q., Lin, F., & Soon, S. H. (2016). GPU accelerated digital volume correlation. Experimental Mechanics, 2(56), 297–309.

    Article  Google Scholar 

  28. Movania, M. M., & Lin, F. (2012). High-performance volume rendering on the ubiquitous WebGL platform. In 2012 IEEE 14th international conference on high performance computing and communication & 2012 IEEE 9th international conference on embedded software and systems, Liverpool, UK. 2012-01-01.

  29. Movania, M. M., & Lin, F. (2012). Ubiquitous medical volume rendering on mobile devices. In International conference on information society (i-Society 2012), London, UK. 2012-01-01.

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Authors and Affiliations

  1. Shanghai Institute of Medical Imaging, Shanghai, 200032, China

    Zhi-ting Xu, Ben-gang Wang & Tian-tian Fu

  2. Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, 200032, China

    Zhi-ting Xu, Hong Ding, Ben-gang Wang, Tian-tian Fu, Yu-li Zhu & Wen-ping Wang

  3. School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Feng Lin

Authors
  1. Zhi-ting Xu
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  2. Hong Ding
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  4. Tian-tian Fu
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  5. Yu-li Zhu
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  7. Feng Lin
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Correspondence to Hong Ding.

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Source of financial support: National Natural Science Fundation of China (81571675, 81873897)

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Xu, Zt., Ding, H., Wang, Bg. et al. Savitzky-Golay Filter Based Quantitative Dynamic Contrast-Enhanced Ultrasound on Assessing Therapeutic Response in Mice with Hepatocellular Carcinoma. J Sign Process Syst 92, 315–323 (2020). https://doi.org/10.1007/s11265-019-01500-6

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  • DOI: https://doi.org/10.1007/s11265-019-01500-6

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