Table of Contents Author Guidelines Submit a Manuscript
Contrast Media & Molecular Imaging
Volume 2019, Article ID 4507694, 9 pages
https://doi.org/10.1155/2019/4507694
Research Article

Ability of 18F-FDG PET/CT Radiomic Features to Distinguish Breast Carcinoma from Breast Lymphoma

1West China School of Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China
2State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
3School of Computer Science, Nanjing University of Science and Technology, No. 200, Xiaolinwei Road, Nanjing 210094, China
4Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, China
5Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, China

Correspondence should be addressed to Rong Tian; moc.621@raelcunnaitgnor and Xuelei Ma; moc.liamg@ieleuxamrd

Received 9 October 2018; Accepted 5 December 2018; Published 25 February 2019

Academic Editor: Guillermina Ferro-Flores

Copyright © 2019 Xuejin Ou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. J. Qiao, J. Yu, Z. Yu, N. Li, C. Song, and M. Li, “Contrast-enhanced ultrasonography in differential diagnosis of benign and malignant ovarian tumors,” PLoS One, vol. 10, no. 3, Article ID e0118872, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. Q. Min, K. Shao, L. Zhai et al., “Differential diagnosis of benign and malignant breast masses using diffusion-weighted magnetic resonance imaging,” World Journal of Surgical Oncology, vol. 13, no. 1, p. 32, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. F. Todua and S. Chedia, “Differentiation between benign and malignant meningiomas using diffusion and perfusion MR imaging,” Georgian Med News, vol. 206, pp. 16–22, 2012. View at Google Scholar
  4. A. Thomas, B. K. Link, S. Altekruse, P. A. Romitti, and M. C. Schroeder, “Primary breast lymphoma in the United States: 1975–2013,” NCI: Journal of the National Cancer Institute, vol. 109, no. 6, 2017. View at Google Scholar
  5. R. S. Offodile, V. Arce, J. Cross, J. Reed, and D. J. Beech, “Primary breast lymphoma: a rare clinical entity,” World Journal of Oncology, vol. 2, no. 3, pp. 147–150, 2011. View at Publisher · View at Google Scholar
  6. N. Anne and R. Pallapothu, “Lymphoma of the breast: a mimic of inflammatory breast cancer,” World Journal of Surgical Oncology, vol. 9, no. 1, p. 125, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Kuroda, J.-i. Tamaru, I. Takeuchi et al., “Primary diffuse large B-cell lymphoma of the breast,” Breast Cancer, vol. 14, no. 3, pp. 317–322, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. G. W. Sledge, E. P. Mamounas, G. N. Hortobagyi, H. J. Burstein, P. J. Goodwin, and A. C. Wolff, “Past, present, and future challenges in breast cancer treatment,” Journal of Clinical Oncology, vol. 32, no. 19, pp. 1979–1986, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Ryan, G. Martinelli, M. Kuper-Hommel et al., “Primary diffuse large B cell lymphoma of the breast: prognostic factors and outcomes of a study by the international extra nodal lymphoma study group,” Annals of Oncology, vol. 19, no. 2, pp. 233–241, 2009. View at Google Scholar
  10. H. Y. Yhim, H. J. Kang, Y. H. Choi et al., “Clinical outcomes and prognostic factors in patients with breast diffuse large B cell lymphoma; consortium for improving survival of lymphoma (CISL) study,” BMC Cancer, vol. 10, no. 1, p. 321, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. B. Coiffier, E. Lepage, J. Brière et al., “CHOP chemotherapy plus rituximab compared with CHOP alone in elderly patients with diffuse large-B-cell lymphoma,” New England Journal of Medicine, vol. 346, no. 4, pp. 235–242, 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Pfreundschuh, L. Trümper, A. Österborg et al., “CHOP-like chemotherapy plus rituximab versus CHOP-like chemotherapy alone in young patients with good-prognosis diffuse large-B-cell lymphoma: a randomised controlled trial by the MabThera International Trial (MInT) Group,” The Lancet Oncology, vol. 7, no. 5, pp. 379–391, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. B. Coiffier, C. Thieblemont, E. Van Den Neste et al., “Long-term outcome of patients in the LNH-98.5 trial, the first randomized study comparing rituximab-CHOP to standard CHOP chemotherapy in DLBCL patients: a study by the groupe d’etudes des lymphomes de l’adulte,” Blood, vol. 116, no. 12, pp. 2040–2045, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. T. M. Habermann, E. A. Weller, V. A. Morrison et al., “Rituximab-CHOP versus CHOP alone or with maintenance rituximab in older patients with diffuse large B-cell lymphoma,” Journal of Clinical Oncology, vol. 24, no. 19, pp. 3121–3127, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Kassner and R. E. Thornhill, “Texture analysis: a review of neurologic MR imaging applications,” American Journal of Neuroradiology, vol. 31, no. 5, pp. 809–816, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Chicklore, V. Goh, M. Siddique, A. Roy, P. K. Marsden, and G. J. R. Cook, “Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 40, no. 1, pp. 133–140, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. P. Gibbs and L. W. Turnbull, “Textural analysis of contrast-enhanced MR images of the breast,” Magnetic Resonance in Medicine, vol. 50, no. 1, pp. 92–98, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. D.-R. Chen, R.-F. Chang, and Y.-L. Huang, “Computer-aided diagnosis applied to US of solid breast nodules by using neural networks,” Radiology, vol. 213, no. 2, pp. 407–412, 1999. View at Publisher · View at Google Scholar · View at Scopus
  19. C. Dennie, R. Thornhill, V. Sethi-Virmani et al., “Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules,” Quantitative Imaging in Medicine and Surgery, vol. 6, no. 1, pp. 6–15, 2016. View at Publisher · View at Google Scholar · View at Scopus
  20. T. Hodgdon, M. D. F. McInnes, N. Schieda, T. A. Flood, L. Lamb, and R. E. Thornhill, “Can quantitative CT texture analysis be used to differentiate fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT images?” Radiology, vol. 276, no. 3, pp. 787–796, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. N. Schieda, R. E. Thornhill, M. Al-Subhi et al., “Diagnosis of sarcomatoid renal cell carcinoma with CT: evaluation by qualitative imaging features and texture analysis,” American Journal of Roentgenology, vol. 204, no. 5, pp. 1013–1023, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Xu, S. Kido, K. Suga et al., “Texture analysis on 18F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions,” Annals of Nuclear Medicine, vol. 28, no. 9, pp. 926–935, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Kirienko, L. Cozzi, A. Rossi et al., “Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 45, no. 10, pp. 1649–1660, 2018. View at Publisher · View at Google Scholar · View at Scopus
  24. C. Nicolau, E. Sala, A. Kumar et al., “Renal masses detected on FDG PET/CT in patients with lymphoma: imaging features differentiating primary renal cell carcinomas from renal lymphomatous involvement,” American Journal of Roentgenology, vol. 208, no. 4, pp. 849–853, 2017. View at Publisher · View at Google Scholar · View at Scopus
  25. X.-H. Ye, L.-H. Chen, H.-B. Wu et al., “18F-FDG PET/CT evaluation of lymphoma with renal involvement: comparison with renal carcinoma,” Southern Medical Journal, vol. 103, no. 7, pp. 642–649, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. C. Nioche, F. Orlhac, S. Boughdad et al., “LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity,” Cancer Research, vol. 78, no. 16, pp. 4786–4789, 2018. View at Publisher · View at Google Scholar · View at Scopus
  27. M. I. Daoud, T. M. Bdair, M. Al-Najar, and R. Alazrai, “A fusion-based approach for breast ultrasound image classification using multiple-ROI texture and morphological analyses,” Computational and Mathematical Methods in Medicine, vol. 2016, Article ID 6740956, 12 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  28. H. Bayanati, R. E. Thornhill, C. A. Souza et al., “Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer?” European Radiology, vol. 25, no. 2, pp. 480–487, 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. J.-b. Qin, Z. Liu, H. Zhang et al., “Grading of gliomas by using radiomic features on multiple magnetic resonance imaging (MRI) sequences,” Medical Science Monitor, vol. 23, pp. 2168–2178, 2017. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Ditmer, B. Zhang, T. Shujaat et al., “Diagnostic accuracy of MRI texture analysis for grading gliomas,” Journal of Neuro-Oncology, vol. 140, no. 3, pp. 583–589, 2018. View at Publisher · View at Google Scholar · View at Scopus
  31. C. Su, J. Jiang, S. Zhang et al., “Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour,” European Radiology, pp. 1–11, 2018. View at Google Scholar
  32. Z. Ma, M. Fang, Y. Huang et al., “CT-based radiomics signature for differentiating borrmann type IV gastric cancer from primary gastric lymphoma,” European Journal of Radiology, vol. 91, pp. 142–147, 2017 Jun. View at Publisher · View at Google Scholar · View at Scopus
  33. H. Minn, H. Joensuu, A. Ahonen, and P. Klemi, “Florodeoxyglucose imaging: a method to assess the proliferative activity of human cancer in vivo. Comparison with DNA flow cytometry in head and neck tumors,” Cancer, vol. 61, no. 9, pp. 1776–1781, 1988. View at Publisher · View at Google Scholar
  34. K. Higashi, Y. Ueda, M. Yagishita, Y. Arisaka, A. Sakurai, M. Oguchi et al., “FDG PET measurement of the proliferative potential of non-small cell lung cancer,” Journal of Nuclear Medicine, vol. 41, pp. 85–92, 2000. View at Google Scholar
  35. Y. Shou, J. Lu, T. Chen, D. Ma, and L. Tong, “Correlation of fluorodeoxyglucose uptake and tumor-proliferating antigen Ki-67 in lymphomas,” Journal of Cancer Research and Therapeutics, vol. 8, no. 1, pp. 96–102, 2012. View at Publisher · View at Google Scholar · View at Scopus
  36. A. Depeursinge, A. Foncubierta-Rodriguez, D. Van De Ville, and H. Müller, “Three-dimensional solid texture analysis in biomedical imaging: review and opportunities,” Medical Image Analysis, vol. 18, no. 1, pp. 176–196, 2014. View at Publisher · View at Google Scholar · View at Scopus
  37. F. Tixier, C. C. Le Rest, M. Hatt et al., “Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer,” Journal of Nuclear Medicine, vol. 52, no. 3, pp. 369–378, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. W. T. Yang, D. L. Lane, H. T. Le-Petross, L. V. Abruzzo, and H. A. Macapinlac, “Breast lymphoma: imaging findings of 32 tumors in 27 patients,” Radiology, vol. 245, no. 3, pp. 692–702, 2007. View at Publisher · View at Google Scholar · View at Scopus