title: Characterizing gradient echo signal decays in gynecologic cancers at 3T using a Gaussian augmentation of the monoexponential (GAME) model.
abstract: To assess whether R2* mapping with a standard Monoexponential (ME) or a Gaussian Augmentation of the Monoexponential (GAME) decay model better characterizes gradient-echo signal decays in gynecological cancers after external beam radiation therapy at 3T, and evaluate implications of modeling for noninvasive identification of intratumoral hypoxia.
  
  
  
  Multi-gradient-echo signals were acquired on 25 consecutive patients with gynecologic cancers and three healthy participants during inhalation of different oxygen concentrations at 3T. Data were fitted with both ME and GAME models. Models were compared using F-tests in tumors and muscles in patients, muscles, cervix, and uterus in healthy participants, and across oxygenation levels.
  
  
  
  GAME significantly improved fitting over ME (P < 0.05): Improvements with GAME covered 34% of tumor regions-of-interest on average, ranging from 6% (of a vaginal tumor) to 68% (of a cervical tumor) in individual tumors. Improvements with GAME were more prominent in areas that would be assumed hypoxic based on ME alone, reaching 90% as ME R2* approached 100 Hz. Gradient echo decay parameters at different oxygenation levels were not significantly different (P = 0.81).
  
  
  
  R2* may prove sensitive to hypoxia; however, inaccurate representations of underlying data may limit the success of quantitative assessments. Although the degree to which R2 or σ values correlate with hypoxia remains unknown, improved characterization with GAME increases the potential for determining any correlates of fit parameters with biomarkers, such as oxygenation status. J. MAGN. RESON. IMAGING 2016;44:1020-1030.
authors.affiliation: Biomedical Engineering, Akdeniz University, Antalya, Turkey. pelin.aksit@gmail.com.|Harvard Medical School, Boston, Massachusetts, USA. pelin.aksit@gmail.com.|Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA. pelin.aksit@gmail.com.
authors.forename: Pelin A
authors.initials: PA
authors.lastname: Ciris
authors.affiliation: Harvard Medical School, Boston, Massachusetts, USA.|Radiology, Boston Children's Hospital, Boston, Massachusetts, USA.
authors.forename: Mukund
authors.initials: M
authors.lastname: Balasubramanian
authors.affiliation: Harvard Medical School, Boston, Massachusetts, USA.|Radiation Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
authors.forename: Antonio L
authors.initials: AL
authors.lastname: Damato
authors.affiliation: Siemens Healthcare, Boston, Massachusetts, USA.
authors.forename: Ravi T
authors.initials: RT
authors.lastname: Seethamraju
authors.affiliation: Harvard Medical School, Boston, Massachusetts, USA.|Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
authors.forename: Clare M
authors.initials: CM
authors.lastname: Tempany-Afdhal
authors.affiliation: Harvard Medical School, Boston, Massachusetts, USA.|Radiology, Boston Children's Hospital, Boston, Massachusetts, USA.
authors.forename: Robert V
authors.initials: RV
authors.lastname: Mulkern
authors.affiliation: Harvard Medical School, Boston, Massachusetts, USA.|Siemens Healthcare, Boston, Massachusetts, USA.
authors.forename: Akila N
authors.initials: AN
authors.lastname: Viswanathan
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000378:metabolism*; D003198:Computer Simulation; D005260:Female; D005833:Genital Neoplasms, Female / Q000000981:diagnostic imaging* / Q000378:metabolism*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D015233:Models, Statistical; D016011:Normal Distribution; D010100:Oxygen / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D000072258:Tumor Hypoxia
chemical_list: D014408:Biomarkers, Tumor; D010100:Oxygen
keywords: Gaussian; Lorentzian; biophysical modeling; blood oxygenation; gynecologic cancers; transverse relaxation