A population-based Gaussian mixture model incorporating 18F-FDG PET and diffusion-weighted MRI quantifies tumor tissue classes

Journal of Nuclear Medicine

Volumn 57, Issue 3, 2016, Pages 473-479

  Divine, Mathew R ^a   Katiyar, Prateek ^a,b   Kohlhofer, Ursula ^c   Quintanilla Martinez, Leticia ^c   Pichler, Bernd J ^a   Disselhorst, Jonathan A ^a  

^a UNIVERSITY OF TÜBINGEN   (Germany)

^b MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^c UNIVERSITY HOSPITAL TÜBINGEN   (Germany)

Author keywords

18F FDG; DWI; Gaussian mixture model; PET MR

Indexed keywords

FLUORODEOXYGLUCOSE F 18; RADIOPHARMACEUTICAL AGENT;

ANIMAL EXPERIMENT; ANIMAL MODEL; ANIMAL TISSUE; APPARENT DIFFUSION COEFFICIENT; ARTICLE; CONTROLLED STUDY; DIFFUSION COEFFICIENT; DIFFUSION WEIGHTED IMAGING; GAUSSIAN MIXTURE MODEL; HISTOLOGY; MATHEMATICAL MODEL; MOUSE; NONHUMAN; POSITRON EMISSION TOMOGRAPHY; PRIORITY JOURNAL; TUMOR MICROENVIRONMENT; TUMOR NECROSIS; TUMOR VOLUME; TUMOR XENOGRAFT; ANIMAL; CANCER TRANSPLANTATION; CLASSIFICATION; DISEASE COURSE; DRUG EFFECTS; HUMAN; MITOSIS; MULTIMODAL IMAGING; NECROSIS; NEOPLASM; PATHOLOGY; PROCEDURES; SCINTISCANNING; THREE DIMENSIONAL IMAGING;

ANIMALS; DIFFUSION MAGNETIC RESONANCE IMAGING; DISEASE PROGRESSION; FLUORODEOXYGLUCOSE F18; HUMANS; IMAGING, THREE-DIMENSIONAL; MICE; MITOSIS; MULTIMODAL IMAGING; NECROSIS; NEOPLASM TRANSPLANTATION; NEOPLASMS; POSITRON-EMISSION TOMOGRAPHY; RADIOPHARMACEUTICALS; TUMOR MICROENVIRONMENT;

EID: 84960113203     PISSN: 01615505     EISSN: 2159662X     Source Type: Journal    
DOI: 10.2967/jnumed.115.163972     Document Type: Article

References (32)

1. Wehrl HF, Sauter AW, Divine MR, Pichler BJ. Combined PET/MR: A technology becomes mature. J Nucl Med. 2015;56: 165-168.
2. Komori T, Narabayashi I, Matsumura K, et al. 2-[fluorine-18]-fluoro-2-deoxy-Dglucose positron emission tomography/computed tomography versus whole-body diffusion-weighted MRI for detection of malignant lesions: Initial experience. Ann Nucl Med. 2007;21: 209-215.
3. Usuda K, Zhao XT, Sagawa M, et al. Diffusion-weighted imaging is superior to positron emission tomography in the detection and nodal assessment of lung cancers. Ann Thorac Surg. 2011;91: 1689-1695.
4. Regier M, Derlin T, Schwarz D, et al. Diffusion weighted MRI and 18F-FDG PET/CT in non-small cell lung cancer (NSCLC): Does the apparent diffusion coefficient (ADC) correlate with tracer uptake (SUV)? Eur J Radiol. 2012;81: 2913-2918.
5. Herneth AM. Apparent diffusion coefficient: A quantitative parameter for in vivo tumor characterization. Eur J Radiol. 2003;45: 208-213.
6. Matoba M, Tonami H, Kondou T, et al. Lung carcinoma: Diffusion-weighted MR imaging-preliminary evaluation with apparent diffusion coefficient. Radiology. 2007;243: 570-577.
7. Moffat BA, Chenevert TL, Lawrence TS, et al. Functional diffusion map: A noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci USA. 2005;102: 5524-5529.
8. Schraml C, Schwenzer NF, Martirosian P, et al. Diffusion-weighted MRI of advanced hepatocellular carcinoma during sorafenib treatment: Initial results. AJR. 2009;193: W301-307.
9. Sauter AW, Schwenzer N, Divine MR, Pichler BJ, Pfannenberg C. Image-derived biomarkers and multimodal imaging strategies for lung cancer management. Eur J Nucl Med Mol Imaging. 2015;42: 634-643.
10. Antoch G, Stattaus J, Nemat AT, et al. Non-small cell lung cancer: Dual-modality PET/CT in preoperative staging. Radiology. 2003;229: 526-533.
11. Kligerman S, Digumarthy S. Staging of non-small cell lung cancer using integrated PET/CT. AJR. 2009;193: 1203-1211.
12. Weber WA, Petersen V, Schmidt B, et al. Positron emission tomography in nonsmall- cell lung cancer: Prediction of response to chemotherapy by quantitative assessment of glucose use. J Clin Oncol. 2003;21: 2651-2657.
13. Cerfolio RJ, Bryant AS, Ohja B, Bartolucci AA. The maximum standardized uptake values on positron emission tomography of a non-small cell lung cancer predict stage, recurrence, and survival. J Thorac Cardiovasc Surg. 2005;130: 151-159.
14. Huang W, Zhou T, Ma L, et al. Standard uptake value and metabolic tumor volume of 18F-FDG PET/CT predict short-term outcome early in the course of chemoradiotherapy in advanced non-small cell lung cancer. Eur J Nucl Med Mol Imaging. 2011;38: 1628-1635.
15. Tanaka R, Nakazato Y, Horikoshi H, Tsuchida S, Yoshida T. Diffusionweighted imaging and positron emission tomography in various cytological subtypes of primary lung adenocarcinoma. Clin Imaging. 2013;37: 876-883.
16. Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45: 228-247.
17. Schmidt H, Brendle C, Schraml C, et al. Correlation of simultaneously acquired diffusion-weighted imaging and 2-deoxy-[18F] fluoro-2-D-glucose positron emission tomography of pulmonary lesions in a dedicated whole-body magnetic resonance/positron emission tomography system. Invest Radiol. 2013;48: 247-255.
18. Jain AK. Data clustering: 50 years beyond K-means. Pattern Recognit Lett. 2010;31: 651-666.
19. Berry LR, Barck KH, Go MA, et al. Quantification of viable tumor microvascular characteristics by multispectral analysis. Magn Reson Med. 2008;60: 64-72.
20. Carano RA, Ross AL, Ross J, et al. Quantification of tumor tissue populations by multispectral analysis. Magn Reson Med. 2004;51: 542-551.
21. Henning EC, Azuma C, Sotak CH, Helmer KG. Multispectral quantification of tissue types in a RIF-1 tumor model with histological validation. Part I. Magn Reson Med. 2007;57: 501-512.
22. Taouli B, Koh D-M. Diffusion-weighted MR imaging of the liver. Radiology. 2010;254: 47-66.
23. Sun X, Niu G, Chan N, Shen B, Chen X. Tumor hypoxia imaging. Mol Imaging Biol. 2011;13: 399-410.
24. Rajendran JG. Hypoxia and glucose metabolism in malignant tumors: Evaluation by [18F]fluoromisonidazole and [18F]fluorodeoxyglucose positron emission tomography imaging. Clin Cancer Res. 2004;10: 2245-2252.
25. Koukourakis MI, Giatromanolaki A, Skarlatos J, et al. Hypoxia inducible factor (HIF-1a and HIF-2a) expression in early esophageal cancer and response to photodynamic therapy and radiotherapy. Cancer Res. 2001;122: 1830-1832.
26. Vleugel MM, Greijer AE, Shvarts A, et al. Differential prognostic impact of hypoxia induced and diffuse HIF-1alpha expression in invasive breast cancer. J Clin Pathol. 2005;58: 172-177.
27. Zhong H, De Marzo AM, Laughner E, et al. Overexpression of hypoxia-inducible factor 1alpha in common human cancers and their metastases. Cancer Res. 1999;59: 5830-5835.
28. Nakajo M, Nakajo M, Kajiya Y, et al. FDG PET/CT and diffusion-weighted imaging of head and neck squamous cell carcinoma: Comparison of prognostic significance between primary tumor standardized uptake value and apparent diffusion coefficient. Clin Nucl Med. 2012;37: 475-480.
29. Baba S, Isoda T, Maruoka Y, et al. Diagnostic and prognostic value of pretreatment SUV in 18F-FDG/PET in breast cancer: Comparison with apparent diffusion coefficient from diffusion-weighted MR imaging. J Nucl Med. 2014;55: 736-742.
30. Rakheja R, Chandarana H, DeMello L, et al. Correlation between standardized uptake value and apparent diffusion coefficient of neoplastic lesions evaluated with whole-body simultaneous hybrid PET/MRI. AJR. 2013;201: 1115-1119.
31. Judenhofer MS, Wehrl HF, Newport DF, et al. Simultaneous PET-MRI: A new approach for functional and morphological imaging. Nat Med. 2008;14: 459-465.
32. Hiraoka N, Ino Y, Sekine S, et al. Tumour necrosis is a postoperative prognostic marker for pancreatic cancer patients with a high interobserver reproducibility in histological evaluation. Br J Cancer. 2010;103: 1057-1065.