Multiexponential T2 relaxometry of benign and malignant adipocytic tumours

Springer Science and Business Media LLC - Tập 4 - Trang 1-11 - 2020
Katerina Nikiforaki1,2, Georgios S. Ioannidis1,2, Eleni Lagoudaki3, Georgios H. Manikis1,2, Eelco de Bree4, Apostolos Karantanas1,2,5, Thomas G. Maris1,2,6, Kostas Marias1,7
1Computational Bio-Medicine Laboratory (CBML), Institute of Computer Science (ICS), Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece
2Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece
3Department of Pathology, University Hospital of Crete, Heraklion, Greece
4Department of Surgical Oncology, University Hospital of Crete, Heraklion, Greece
5Department of Medical Imaging, University Hospital, Heraklion, Greece
6Department of Medical Physics, University of Crete, Heraklion, Greece
7Department of Electrical & Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece

Tóm tắt

We investigated a recently proposed multiexponential (Mexp) fitting method applied to T2 relaxometry magnetic resonance imaging (MRI) data of benign and malignant adipocytic tumours and healthy subcutaneous fat. We studied the T2 distributions of the different tissue types and calculated statistical metrics to differentiate benign and malignant tumours. Twenty-four patients with primary benign and malignant adipocytic tumours prospectively underwent 1.5-T MRI with a single-slice T2 relaxometry (Carr-Purcell-Meiboom-Gill sequence, 25 echoes) prior to surgical excision and histopathological assessment. The proposed method adaptively chooses a monoexponential or biexponential model on a voxel basis based on the adjusted R2 goodness of fit criterion. Linear regression was applied on the statistical metrics derived from the T2 distributions for the classification. Healthy subcutaneous fat and benign lipoma were better described by biexponential fitting with a monoexponential and biexponential prevalence of 0.0/100% and 0.2/99.8% respectively. Well-differentiated liposarcomas exhibit 17.6% monoexponential and 82.4% biexponential behaviour, while more aggressive liposarcomas show larger degree of monoexponential behaviour. The monoexponential/biexponential prevalence was 47.6/52.4% for myxoid tumours, 52.8/47.2% for poorly differentiated parts of dedifferentiated liposarcomas, and 24.9/75.1% pleomorphic liposarcomas. The percentage monoexponential or biexponential model prevalence per patient was the best classifier distinguishing between malignant and benign adipocytic tumours with a 0.81 sensitivity and a 1.00 specificity. Healthy adipose tissue and benign lipomas showed a pure biexponential behaviour with similar T2 distributions, while decreased adipocytic cell differentiation characterising aggressive neoplasms was associated with an increased rate of monoexponential decay curves, opening a perspective adipocytic tumour classification.

Tài liệu tham khảo

Goldblum JR, Folpe AL, Weiss SW (2013) Enzinger and Weiss’s soft tissue tumors. Elsevier Saunders, Philadelphia Guillou L, Coindre JM, Bonichon F et al (1997) Comparative study of the National Cancer Institute and French Federation of Cancer Centers Sarcoma Group grading systems in a population of 410 adult patients with soft tissue sarcoma. J Clin Oncol 15:350–362. https://doi.org/10.1200/JCO.1997.15.1.350 Fletcher CD (2006) The evolving classification of soft tissue tumours: an update based on the new WHO classification. Histopathology 48(1):3–12. https://doi.org/10.1111/j.1365-2559.2005.02284.x Callegaro D, Miceli R, Bonvalot S et al (2018) Impact of perioperative chemotherapy and radiotherapy in patients with primary extremity soft tissue sarcoma: retrospective analysis across major histological subtypes and major reference centres. Eur J Cancer 105:19–27. https://doi.org/10.1016/j.ejca.2018.09.028 Casali PG, Abecassis N, Aro HT et al (2018) Soft tissue and visceral sarcomas: ESMO–EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up†. Ann Oncol 29:iv51–iv67. https://doi.org/10.1093/annonc/mdy096 Raj A, Pandya S, Shen X, LoCastro E, Nguyen TD, Gauthier SA (2014) Multi-compartment T2 relaxometry using a spatially constrained multi-Gaussian model. PLoS One 9:e98391. https://doi.org/10.1371/journal.pone.0098391 Reiter DA, Lin P-C, Fishbein KW, Spencer RG (2009) Multicomponent T 2 relaxation analysis in cartilage. Magn Reson Med 61:803–809. https://doi.org/10.1002/mrm.21926 Does MD, Snyder RE (1995) T2 Relaxation of peripheral nerve measured in vivo. Magn Reson Imaging 13:575–580. https://doi.org/10.1016/0730-725X(94)00138-S Chaland B, Mariette F, Marchal P, Certaines J DE (2019) 1H nuclear magnetic resonance relaxometric characterization of fat and water states in soft and hard cheese. J Dairy Res 67:609–618. https://doi.org/10.1017/S0022029900004398 Maris TG, Pappas E, Boursianis T et al (2016) 3D polymer gel MRI dosimetry using a 2D haste, A 2D TSE and A 2D SE multi echo (ME) T2 relaxometric sequences: comparison of dosimetric results. Phys Medica 32:238–239. https://doi.org/10.1016/J.EJMP.2016.07.498 Loskutov V, Zhakov S (2016) Dependence of the liquid transverse relaxation time T2 in porous media on fluid flow velocity. Int J Heat Mass Transf 101:692–698. https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2016.05.057 Sobol WT, Pintar MM (1987) NMR spectroscopy of heterogeneous solid-liquid mixtures. Spin grouping and exchange analysis of proton spin relaxation in a tissue. Magn Reson Med 4:537–554. https://doi.org/10.1002/mrm.1910040605 Vasilescu V, Katona E, Simplâceanu V, Demco D (1978) Water compartments in the myelinated nerve. III. Pulsed NMR result. Experientia 34:1443–1444. https://doi.org/10.1007/BF01932339 Mulkern RV, Bleier AR, Adzamli K, Spencer RGS, Sandor T, Jolesz FA (1989) Two-site exchange revisited: a new method for extracting exchange parameters in biological systems. Biophys J 55:221–232. https://doi.org/10.1016/S0006-3495(89)82797-3 Brix G, Heiland S, Bellemann ME, Koch T, Lorenz WJBrix G, Heiland S, Bellemann ME, Koch T, Lorenz WJ (1993) MR imaging of fat-containing tissues: valuation of two quantitative imaging techniques in comparison with localized proton spectroscopy. Magn Reson Imaging 11:977–991. https://doi.org/10.1016/0730-725X(93)90217-2 Ioannidis GS, Nikiforaki K, Kalaitzakis G, Karantanas A, Marias K, Maris TG (2020) Inverse Laplace transform and multiexponential fitting analysis of T2 relaxometry data: a phantom study with aqueous and fat containing samples. Eur Radiol Exp 4:28. https://doi.org/10.1186/s41747-020-00154-5 Fransson A, Ericsson A, Jung B, Sperber GO (1993) Properties of the phase-alternating phase-shift (PHAPS) multiple spin-echo protocol in MRI: a study of the effects of imperfect RF pulses. Magn Reson Imaging 11:771–784. https://doi.org/10.1016/0730-725X(93)90195-J Hennig J (1991) Echoes—how to generate, recognize, use or avoid them in MR-imaging sequences. Part I: fundamental and not so fundamental properties of spin echoes. Concepts Magn Reson 3:125–143. https://doi.org/10.1002/cmr.1820030302 Milford D, Rosbach N, Bendszus M, Heiland S (2015) Mono-exponential fitting in T2-relaxometry: relevance of offset and first echo. PLoS One 10:e0145255. https://doi.org/10.1371/journal.pone.0145255 MacKay A, Whittall K, Adler J, Li D, Paty D, Graeb D (1994) In vivo visualization of myelin water in brain by magnetic resonance. Magn Reson Med 31:673–677. https://doi.org/10.1002/mrm.1910310614 Whittall KP, MacKay AL, Graeb DA, Nugent RA, Li DK, Paty DW (1997) In vivo measurement of T2 distributions and water contents in normal human brain. Magn Reson Med 37:34–43. https://doi.org/10.1002/mrm.1910370107 Saab G, Thompson RT, Marsh GD, Picot PA, Moran GRSaab G, Thompson RT, Marsh GD, Picot PA, Moran GR (2001) Two-dimensional time correlation relaxometry of skeletal muscle in vivo at 3 Tesla. Magn Reson Med 46:1093–1098. https://doi.org/10.1002/mrm.1304 Torriani M, Taneja AK, Hosseini A, Gill TJ, Bredella MA, Li G. (2014) T2 relaxometry of the infrapatellar fat pad after arthroscopic surgery. Skeletal Radiol 43:315–321. https://doi.org/10.1007/s00256-013-1791-4 Pedregosa F, Varoquaux G, Gramfort A et al (2012) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830 Alonso-Ortiz E, Levesque IR, Pike GB (2015) MRI-based myelin water imaging: a technical review. Magn Reson Med 73:70–81. https://doi.org/10.1002/mrm.25198 Kimura M, Takemori S, Yamaguchi M, Umazume Y (2005) Differential osmotic behavior of water components in living skeletal muscle resolved by 1H-NMR. Biophys J. https://doi.org/10.1529/biophysj.105.059717 Chaudhari AS, Sveinsson B, Moran CJ et al (2017) Imaging and T2 relaxometry of short-T2 connective tissues in the knee using ultrashort echo-time double-echo steady-state (UTEDESS). Magn Reson Med 78:2136–2148. https://doi.org/10.1002/mrm.26577 Millis K, Weybright P, Campbell N et al (1999) Classification of human liposarcoma and lipoma using ex vivo proton NMR spectroscopy. Magn Reson Med 41:257–267. https://doi.org/10.1002/(sici)1522-2594(199902)41:2<257::aid-mrm8>3.0.co;2-n Chen JH, Enloe BM, Fletcher CD, Cory DG, Singer S (2001) Biochemical analysis using high-resolution magic angle spinning NMR spectroscopy distinguishes lipoma-like well-differentiated liposarcoma from normal fat. J Am Chem Soc 123:9200-9201. https://doi.org/10.1021/JA016182U Juchem C, de Graaf RA (2017) B0 magnetic field homogeneity and shimming for in vivo magnetic resonance spectroscopy. Anal Biochem 529:17–29. https://doi.org/10.1016/j.ab.2016.06.003 Constable RT, Anderson AW, Zhong J, Gore JC (1992) Factors influencing contrast in fast spin-echo MR imaging. Magn Reson Imaging 10:497–511. https://doi.org/10.1016/0730-725X(92)90001-G Liu L, Yin B, Geng DY, Lu YP, Peng WJ (2016) Changes of T2 relaxation time from neoadjuvant chemotherapy in breast cancer lesions. Iran J Radiol 13:e24014. https://doi.org/10.5812/iranjradiol.24014
Thông tin
Thông tin xuất bản

Springer Science and Business Media LLC

Tập 4

1-11

Thông tin tác giả