Table of Contents Author Guidelines Submit a Manuscript
Contrast Media & Molecular Imaging
Volume 2018, Article ID 5076269, 11 pages
https://doi.org/10.1155/2018/5076269
Research Article

DCE-MRI Pharmacokinetic-Based Phenotyping of Invasive Ductal Carcinoma: A Radiomic Study for Prediction of Histological Outcomes

1IRCCS SDN, Naples, Italy
2Department of Pathology, Ospedale Moscati, Avellino, Italy

Correspondence should be addressed to Marco Aiello; ti.ilopan-nds@olleiam

Received 28 July 2017; Revised 20 November 2017; Accepted 18 December 2017; Published 17 January 2018

Academic Editor: Isabella Castiglioni

Copyright © 2018 Serena Monti 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. R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, 2016,” CA: A Cancer Journal for Clinicians, vol. 66, no. 1, pp. 7–30, 2016. View at Publisher · View at Google Scholar
  2. L. A. Torre, R. L. Siegel, E. M. Ward, and A. Jemal, “Global cancer incidence and mortality rates and trends—an update,” Cancer Epidemiology, Biomarkers & Prevention, vol. 25, no. 1, pp. 16–27, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Zimmer, M. Gatti-Mays, S. Soltani et al., “Abstract PD6-01: Analysis of breast cancer in young women in the department of defense (DOD) database,” American Association for Cancer Research, 2017. View at Google Scholar
  4. L. J. van't Veer, H. Dai, M. J. van de Vijver et al., “Gene expression profiling predicts clinical outcome of breast cancer,” Nature, vol. 415, no. 6871, pp. 530–536, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. T. Sørlie, C. M. Perou, and R. Tibshirani, “Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 98, no. 19, pp. 10869–10874, 2001. View at Publisher · View at Google Scholar
  6. K. E. Huber, L. A. Carey, and D. E. Wazer, “Breast cancer molecular subtypes in patients with locally advanced disease: impact on prognosis, patterns of recurrence, and response to therapy,” in Seminars in Radiation Oncology, Elsevier, 2009. View at Google Scholar
  7. A. Goldhirsch, W. C. Wood, A. S. Coates, R. D. Gelber, B. Thürlimann, and H.-J. Senn, “Strategies for subtypes-dealing with the diversity of breast cancer: highlights of the St Gallen international expert consensus on the primary therapy of early breast cancer 2011,” Annals of Oncology, vol. 22, no. 8, pp. 1736–1747, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. F. C. Geyer, D. N. Rodrigues, B. Weigelt, and J. S. Reis-Filho, “Molecular classification of estrogen receptor-positive/luminal breast cancers,” Advances in Anatomic Pathology, vol. 19, no. 1, pp. 39–53, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. Cancer Genome Atlas Network, “Comprehensive molecular portraits of human breast tumours,” Nature, vol. 490, pp. 61–70, 2012. View at Publisher · View at Google Scholar
  10. S. P. Bagaria, P. S. Ray, M.-S. Sim et al., “Personalizing breast cancer staging by the inclusion of ER, PR, and HER2,” JAMA Surgery, vol. 149, no. 2, pp. 125–129, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. C. M. Perou, T. Sørile, M. B. Eisen et al., “Molecular portraits of human breast tumours,” Nature, vol. 406, no. 6797, pp. 747–752, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. N. Harbeck, C. Thomssen, and M. Gnant, “St. Gallen 2013: brief preliminary summary of the consensus discussion,” Breast Care, vol. 8, no. 2, pp. 102–109, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Bustreo, S. Osella-Abate, P. Cassoni et al., “Optimal Ki67 cut-off for luminal breast cancer prognostic evaluation: a large case series study with a long-term follow-up,” Breast Cancer Research and Treatment, vol. 157, no. 2, pp. 363–371, 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. E. J. Sutton, J. H. Oh, B. Z. Dashevsky et al., “Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay,” Journal of Magnetic Resonance Imaging, vol. 42, no. 5, pp. 1398–1406, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. M. C. U. Cheang, S. K. Chia, D. Voduc et al., “Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer,” Journal of the National Cancer Institute, vol. 101, no. 10, pp. 736–750, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Fan, H. Li, S. Wang, B. Zheng, J. Zhang, and L. Li, “Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer,” PLoS ONE, vol. 12, no. 2, Article ID e0171683, 2017. View at Publisher · View at Google Scholar · View at Scopus
  17. O. Metzger-Filho, Z. Sun, G. Viale et al., “Patterns of recurrence and outcome according to breast cancer subtypes in lymph node-negative disease: Results from international breast cancer study group trials VIII and IX,” Journal of Clinical Oncology, vol. 31, no. 25, pp. 3083–3090, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. F. J. Esteva, D. Yu, M.-C. Hung, and G. N. Hortobagyi, “Molecular predictors of response to trastuzumab and lapatinib in breast cancer,” Nature Reviews Clinical Oncology, vol. 7, no. 2, pp. 98–107, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. K. Kontzoglou, V. Palla, G. Karaolanis et al., “Correlation between Ki67 and Breast Cancer Prognosis,” Oncology (Switzerland), vol. 84, no. 4, pp. 219–225, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Wang, F. Kato, N. Oyama-Manabe et al., “Identifying triple-negative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI: A pilot radiomics study,” PLoS ONE, vol. 10, no. 11, Article ID e0143308, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. J. R. Egner, “AJCC Cancer Staging Manual,” Journal of the American Medical Association, vol. 304, no. 15, p. 1726, 2010. View at Publisher · View at Google Scholar
  22. D. C. Zaha, “Significance of immunohistochemistry in breast cancer,” World Journal of Clinical Oncology, vol. 5, no. 3, pp. 382–392, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. W. Bruening, J. Fontanarosa, K. Tipton, J. R. Treadwell, J. Launders, and K. Schoelles, “Systematic review: Comparative effectiveness of core-needle and open surgical biopsy to diagnose breast lesions,” Annals of Internal Medicine, vol. 152, no. 4, pp. 238–246, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. K. Tamaki K., H. Sasano, T. Ishida et al., “Comparison of core needle biopsy (CNB) and surgical specimens for accurate preoperative evaluation of ER, PgR and HER2 status of breast cancer patients,” Cancer Science, vol. 101, no. 9, pp. 2074–2079, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Ough, J. Velasco, and T. J. Hieken, “A comparative analysis of core needle biopsy and final excision for breast cancer: Histology and marker expression,” The American Journal of Surgery, vol. 201, no. 5, pp. 685–687, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. D. L. Longo, “Tumor heterogeneity and personalized medicine,” The New England Journal of Medicine, vol. 366, no. 10, pp. 956-957, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. R. A. Gatenby, O. Grove, and R. J. Gillies, “Quantitative imaging in cancer evolution and ecology,” Radiology, vol. 269, no. 1, pp. 8–15, 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. R. J. Gillies, A. R. Anderson, R. A. Gatenby, and D. L. Morse, “The biology underlying molecular imaging in oncology: from genome to anatome and back again,” Clinical Radiology, vol. 65, no. 7, pp. 517–521, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. H. J. Aerts, E. R. Velazquez, R. T. Leijenaar et al., “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach,” Nature Communications, vol. 5, article 4006, 2014. View at Publisher · View at Google Scholar
  30. M. Incoronato, M. Aiello, T. Infante et al., “Radiogenomic Analysis of Oncological Data: A Technical Survey,” International Journal of Molecular Sciences, vol. 18, no. 4, p. 805, 2017. View at Publisher · View at Google Scholar
  31. M. Diehn, C. Nardini, D. S. Wang et al., “Identification of noninvasive imaging surrogates for brain tumor gene-expression modules,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 105, no. 13, pp. 5213–5218, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. E. Segal, C. B. Sirlin, C. Ooi et al., “Decoding global gene expression programs in liver cancer by noninvasive imaging,” Nature Biotechnology, vol. 25, no. 6, pp. 675–680, 2007. View at Publisher · View at Google Scholar · View at Scopus
  33. R. J. Gillies, P. E. Kinahan, and H. Hricak, “Radiomics: images are more than pictures, they are data,” Radiology, vol. 278, no. 2, pp. 563–577, 2016. View at Publisher · View at Google Scholar · View at Scopus
  34. H. Li, Y. Zhu, E. S. Burnside et al., “Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set,” NPJ Breast Cancer, vol. 2, no. 1, 2016. View at Publisher · View at Google Scholar
  35. S. S. F. Yip and H. J. W. L. Aerts, “Applications and limitations of radiomics,” Physics in Medicine and Biology, vol. 61, no. 13, pp. R150–R166, 2016. View at Publisher · View at Google Scholar · View at Scopus
  36. M. Kirienko, L. Cozzi, L. Antunovic et al., “Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 45, no. 2, pp. 1–11, 2017. View at Google Scholar
  37. P. Blanc-Durand, “18F-FDG PET-based Radiomics Score Predicts Survival in Patients treated with Yttrium-90 Transarterial Radioembolization for Unresectable Hepatocellular Carcinoma,” Journal of Nuclear Medicine, vol. 58, no. supplement 1, p. 460, 2017. View at Google Scholar
  38. J. Wang, C.-J. Wu, M.-L. Bao, J. Zhang, X.-N. Wang, and Y.-D. Zhang, “Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer,” European Radiology, vol. 27, no. 10, pp. 4082–4090, 2017. View at Publisher · View at Google Scholar · View at Scopus
  39. A. Jochems, F. Hoebers, D. De Ruysscher et al., “EP-1605: Deep learning of radiomics features for survival prediction in NSCLC and Head and Neck carcinoma,” Radiotherapy & Oncology, vol. 123, p. S866, 2017. View at Publisher · View at Google Scholar
  40. M. Ingrisch, M. J. Schneider, D. Nörenberg et al., “Radiomic analysis reveals prognostic information in T1-weighted baseline magnetic resonance imaging in patients with glioblastoma,” Investigative Radiology, vol. 52, no. 6, pp. 360–366, 2017. View at Publisher · View at Google Scholar · View at Scopus
  41. B. Zhang, J. Tian, D. Dong et al., “Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma,” Clinical Cancer Research, vol. 23, no. 15, pp. 4259–4269, 2017. View at Publisher · View at Google Scholar
  42. K. Pinker, F. Shitano, E. Sala et al., “Background, current role, and potential applications of radiogenomics,” Journal of Magnetic Resonance Imaging, 2017. View at Publisher · View at Google Scholar
  43. F. Gallivanone, M. M. Panzeri, C. Canevari et al., “Biomarkers from in vivo molecular imaging of breast cancer: pretreatment 18F-FDG PET predicts patient prognosis, and pretreatment DWI-MR predicts response to neoadjuvant chemotherapy,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 30, no. 4, pp. 359–373, 2017. View at Publisher · View at Google Scholar · View at Scopus
  44. E. J. Sutton, B. Z. Dashevsky, J. H. Oh et al., “Breast cancer molecular subtype classifier that incorporates MRI features,” Journal of Magnetic Resonance Imaging, vol. 44, no. 1, pp. 122–129, 2016. View at Publisher · View at Google Scholar · View at Scopus
  45. N. Hylton, “Dynamic contrast-enhanced magnetic resonance imaging as an imaging biomarker,” Journal of Clinical Oncology, vol. 24, no. 20, pp. 3293–3298, 2006. View at Publisher · View at Google Scholar · View at Scopus
  46. N. Bhooshan, M. L. Giger, S. A. Jansen, H. Li, L. Lan, and G. M. Newstead, “Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers,” Radiology, vol. 254, no. 3, pp. 680–690, 2010. View at Publisher · View at Google Scholar · View at Scopus
  47. N. Bhooshan, M. Giger, D. Edwards et al., “Computerized three-class classification of MRI-based prognostic markers for breast cancer,” Physics in Medicine and Biology, vol. 56, no. 18, pp. 5995–6008, 2011. View at Publisher · View at Google Scholar · View at Scopus
  48. N. M. Braman, M. Etesami, P. Prasanna et al., “Erratum to: Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI,” Breast Cancer Research, vol. 19, no. 1, p. 57, 2017. View at Publisher · View at Google Scholar
  49. J. Wu, G. Gong, Y. Cui, and R. Li, “Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy,” Journal of Magnetic Resonance Imaging, vol. 44, no. 5, pp. 1107–1115, 2016. View at Publisher · View at Google Scholar · View at Scopus
  50. M. Fan, G. Wu, H. Cheng, J. Zhang, G. Shao, and L. Li, “Radiomic analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients,” European Journal of Radiology, vol. 94, pp. 140–147, 2017. View at Publisher · View at Google Scholar
  51. Y. Zhu, H. Li, W. Guo et al., “Deciphering genomic underpinnings of quantitative MRI-based radiomic phenotypes of invasive breast carcinoma,” Scientific Reports, vol. 5, Article ID 17787, 2015. View at Publisher · View at Google Scholar · View at Scopus
  52. H. Li, Y. Zhu, E. S. Burnside et al., “MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, oncotype DX, and PAM50 gene assays,” Radiology, vol. 281, no. 2, pp. 382–391, 2016. View at Publisher · View at Google Scholar · View at Scopus
  53. S. C. Agner, M. A. Rosen, S. Englander et al., “Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced mr images: A feasibility study,” Radiology, vol. 272, no. 1, pp. 91–99, 2014. View at Publisher · View at Google Scholar · View at Scopus
  54. M. A. Mazurowski, J. Zhang, L. J. Grimm, S. C. Yoon, and J. I. Silber, “Radiogenomic analysis of breast cancer: Luminal B molecular subtype is associated with enhancement dynamics at MR imaging,” Radiology, vol. 273, no. 2, pp. 365–372, 2014. View at Publisher · View at Google Scholar · View at Scopus
  55. L. J. Grimm, J. Zhang, and M. A. Mazurowski, “Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms,” Journal of Magnetic Resonance Imaging, vol. 42, no. 4, pp. 902–907, 2015. View at Publisher · View at Google Scholar · View at Scopus
  56. W. Guo, H. Li, Y. Zhu et al., “Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data,” Journal of Medical Imaging, vol. 2, no. 4, p. 041007, 2015. View at Publisher · View at Google Scholar
  57. E. Blaschke and H. Abe, “MRI phenotype of breast cancer: Kinetic assessment for molecular subtypes,” Journal of Magnetic Resonance Imaging, vol. 42, no. 4, pp. 920–924, 2015. View at Publisher · View at Google Scholar · View at Scopus
  58. K. Yamaguchi, H. Abe, G. M. Newstead et al., “Intratumoral heterogeneity of the distribution of kinetic parameters in breast cancer: comparison based on the molecular subtypes of invasive breast cancer,” Breast Cancer, vol. 22, no. 5, pp. 496–502, 2015. View at Publisher · View at Google Scholar · View at Scopus
  59. H. R. Koo, N. Cho, I. C. Song et al., “Correlation of perfusion parameters on dynamic contrast-enhanced MRI with prognostic factors and subtypes of breast cancers,” Journal of Magnetic Resonance Imaging, vol. 36, no. 1, pp. 145–151, 2012. View at Publisher · View at Google Scholar · View at Scopus
  60. Z. Li, T. Ai, Y. Hu et al., “Application of whole‐lesion histogram analysis of pharmacokinetic parameters in dynamic contrast‐enhanced MRI of breast lesions with the CAIPIRINHA‐Dixon‐TWIST‐VIBE technique,” Journal of Magnetic Resonance Imaging, 2017. View at Google Scholar
  61. A. Fedorov, R. Beichel, J. Kalpathy-Cramer et al., “3D slicer as an image computing platform for the quantitative imaging network,” Magnetic Resonance Imaging, vol. 30, no. 9, pp. 1323–1341, 2012. View at Publisher · View at Google Scholar · View at Scopus
  62. X. Li, L. R. Arlinghaus, G. D. Ayers et al., “DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: Pilot study findings,” Magnetic Resonance in Medicine, vol. 71, no. 4, pp. 1592–1602, 2014. View at Publisher · View at Google Scholar · View at Scopus
  63. P. S. Tofts, “T1-weighted DCE imaging concepts: modelling, acquisition and analysis,” Signal, vol. 500, no. 450, p. 400, 2010. View at Google Scholar
  64. G. Collewet, M. Strzelecki, and F. Mariette, “Influence of MRI acquisition protocols and image intensity normalization methods on texture classification,” Magnetic Resonance Imaging, vol. 22, no. 1, pp. 81–91, 2004. View at Publisher · View at Google Scholar · View at Scopus
  65. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973. View at Publisher · View at Google Scholar · View at Scopus
  66. M. Vallières, C. R. Freeman, S. R. Skamene, and I. El Naqa, “A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities,” Physics in Medicine and Biology, vol. 60, no. 14, article no. 5471, pp. 5471–5496, 2015. View at Publisher · View at Google Scholar · View at Scopus
  67. D. N. Reshef, Y. A. Reshef, H. K. Finucane et al., “Detecting novel associations in large data sets,” Science, vol. 334, no. 6062, pp. 1518–1524, 2011. View at Publisher · View at Google Scholar · View at Scopus
  68. B. Sahiner, H.-P. Chan, and L. Hadjiiski, “Classifier performance prediction for computer-aided diagnosis using a limited dataset,” Medical Physics, vol. 35, no. 4, pp. 1559–1570, 2008. View at Publisher · View at Google Scholar · View at Scopus
  69. J. Juntu, J. Sijbers, S. De Backer, J. Rajan, and D. Van Dyck, “Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images,” Journal of Magnetic Resonance Imaging, vol. 31, no. 3, pp. 680–689, 2010. View at Publisher · View at Google Scholar · View at Scopus
  70. S. Monti, P. Borrelli, E. Tedeschi, S. Cocozza, and G. Palma, “RESUME: Turning an SWI acquisition into a fast qMRI protocol,” PLoS ONE, vol. 12, no. 12, Article ID e0189933, 2017. View at Publisher · View at Google Scholar