View Item 

Show simple item record

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCEâ MRI derived biomarkers in multicenter oncology trials
dc.contributor.authorShukla‐dave, Amita
dc.contributor.authorObuchowski, Nancy A.
dc.contributor.authorChenevert, Thomas L.
dc.contributor.authorJambawalikar, Sachin
dc.contributor.authorSchwartz, Lawrence H.
dc.contributor.authorMalyarenko, Dariya
dc.contributor.authorHuang, Wei
dc.contributor.authorNoworolski, Susan M.
dc.contributor.authorYoung, Robert J.
dc.contributor.authorShiroishi, Mark S.
dc.contributor.authorKim, Harrison
dc.contributor.authorCoolens, Catherine
dc.contributor.authorLaue, Hendrik
dc.contributor.authorChung, Caroline
dc.contributor.authorRosen, Mark
dc.contributor.authorBoss, Michael
dc.contributor.authorJackson, Edward F.
dc.date.accessioned2019-06-20T17:04:44Z
dc.date.availableWITHHELD_13_MONTHS
dc.date.available2019-06-20T17:04:44Z
dc.date.issued2019-06
dc.identifier.citationShukla‐dave, Amita ; Obuchowski, Nancy A.; Chenevert, Thomas L.; Jambawalikar, Sachin; Schwartz, Lawrence H.; Malyarenko, Dariya; Huang, Wei; Noworolski, Susan M.; Young, Robert J.; Shiroishi, Mark S.; Kim, Harrison; Coolens, Catherine; Laue, Hendrik; Chung, Caroline; Rosen, Mark; Boss, Michael; Jackson, Edward F. (2019). "Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCEâ MRI derived biomarkers in multicenter oncology trials." Journal of Magnetic Resonance Imaging 49(7): e101-e121.
dc.identifier.issn1053-1807
dc.identifier.issn1522-2586
dc.identifier.urihttps://hdl.handle.net/2027.42/149510
dc.publisherRSNA
dc.publisherWiley Periodicals, Inc.
dc.subject.otherMRI
dc.subject.otherquantitative imaging biomarkers
dc.subject.otherDWI
dc.subject.otherDCE
dc.titleQuantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCEâ MRI derived biomarkers in multicenter oncology trials
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/149510/1/jmri26518_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/149510/2/jmri26518.pdf
dc.identifier.doi10.1002/jmri.26518
dc.identifier.sourceJournal of Magnetic Resonance Imaging
dc.identifier.citedreferenceLehman CD, Gatsonis C, Kuhl CK, et al. MRI evaluation of the contralateral breast in women with recently diagnosed breast cancer. N Engl J Med 2007; 356: 1295 â 1303.
dc.identifier.citedreferenceNewitt DC, Aliu SO, Witcomb N, et al. Realâ time measurement of functional tumor volume by MRI to assess treatment response in breast cancer neoadjuvant clinical trials: Validation of the Aegis SER Software Platform. Transl Oncol 2014; 7: 94 â 100.
dc.identifier.citedreferenceHeisen M, Fan XB, Buurman J, van Riel NAW, Karczmar GS, Romeny BMT. The influence of temporal resolution in determining pharmacokinetic parameters from DCEâ MRI data. Magn Reson Med 2010; 63: 811 â 816.
dc.identifier.citedreferenceDi Giovanni P, Azlan CA, Ahearn TS, Semple SI, Gilbert FJ, Redpath TW. The accuracy of pharmacokinetic parameter measurement in DCEâ MRI of the breast at 3â T. Phys Med Biol 2010; 55: 121 â 132.
dc.identifier.citedreferenceRoberts C, Issa B, Stone A, Jackson A, Waterton JC, Parker GJ. Comparative study into the robustness of compartmental modeling and modelâ free analysis in DCEâ MRI studies. J Magn Reson Imaging 2006; 23: 554 â 563.
dc.identifier.citedreferenceHenderson E, Rutt BK, Lee TY. Temporal sampling requirements for the tracer kinetics modeling of breast disease. Magn Reson Imaging 1998; 16: 1057 â 1073.
dc.identifier.citedreferenceTudorica LA, Oh KY, Roy N, et al. A feasible high spatiotemporal resolution breast DCEâ MRI protocol for clinical settings. Magn Reson Imaging 2012; 30: 1257 â 1267.
dc.identifier.citedreferenceHuang W, Li X, Chen Y, et al. Variations of dynamic contrastâ enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. Transl Oncol 2014; 7: 153 â 166.
dc.identifier.citedreferenceTudorica A, Oh KY, Chui SY, et al. Early prediction and evaluation of breast cancer response to neoadjuvant chemotherapy using quantitative DCEâ MRI. Transl Oncol 2016; 9: 8 â 17.
dc.identifier.citedreferenceSaranathan M, Rettmann DW, Hargreaves BA, Clarke SE, Vasanawala SS. DIfferential Subsampling with Cartesian Ordering (DISCO): a high spatioâ temporal resolution Dixon imaging sequence for multiphasic contrast enhanced abdominal imaging. J Magn Reson Imaging 2012; 35: 1484 â 1492.
dc.identifier.citedreferenceMorrison CK, Henze Bancroft LC, DeMartini WB, et al. Novel high spatiotemporal resolution versus standardâ ofâ care dynamic contrastâ enhanced breast MRI: Comparison of image quality. Invest Radiol 2017; 52: 198 â 205.
dc.identifier.citedreferenceBeck GM, De Becker J, Jones AC, von Falkenhausen M, Willinek WA, Gieseke J. Contrastâ enhanced timing robust acquisition order with a preparation of the longitudinal signal component (CENTRA plus) for 3D contrastâ enhanced abdominal imaging. J Magn Reson Imaging 2008; 27: 1461 â 1467.
dc.identifier.citedreferenceHuang W, Tudorica LA, Li X, et al. Discrimination of benign and malignant breast lesions by using shutterâ speed dynamic contrastâ enhanced MR imaging. Radiology 2011; 261: 394 â 403.
dc.identifier.citedreferenceLi X, Arlinghaus LR, Ayers GD, et al. DCEâ MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: pilot study findings. Magn Reson Med 2014; 71: 1592 â 1602.
dc.identifier.citedreferenceLi X, Kang H, Arlinghaus LR, et al. Analyzing spatial heterogeneity in DCEâ and DWâ MRI parametric maps to optimize prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. Transl Oncol 2014; 7: 14 â 22.
dc.identifier.citedreferenceSchabel MC, Morrell GR, Oh KY, Walczak CA, Barlow RB, Neumayer LA. Pharmacokinetic mapping for lesion classification in dynamic breast MRI. J Magn Reson Imaging 2010; 31: 1371 â 1378.
dc.identifier.citedreferenceVerma S, Turkbey B, Muradyan N, et al. Overview of dynamic contrastâ enhanced MRI in prostate cancer diagnosis and management. AJR Am J Roentgenol 2012; 198: 1277 â 1288.
dc.identifier.citedreferenceWeinreb JC, Barentsz JO, Choyke PL, et al. PIâ RADS Prostate Imaging Reporting and Data System: 2015, Version 2. Eur Urol 2016; 69: 16 â 40.
dc.identifier.citedreferenceStarobinets O, Korn N, Iqbal S, et al. Practical aspects of prostate MRI: hardware and software considerations, protocols, and patient preparation. Abdom Radiol 2016; 41: 817 â 830.
dc.identifier.citedreferenceRosenkrantz AB, Geppert C, Grimm R, et al. Dynamic contrastâ enhanced MRI of the prostate with high spatiotemporal resolution using compressed sensing, parallel |imaging, and continuous goldenâ angle radial sampling: preliminary experience. J Magn Reson Imaging 2015; 41: 1365 â 1373.
dc.identifier.citedreferenceChung S, Kim D, Breton E, Axel L. Rapid B1â +â mapping using a preconditioning RF pulse with TurboFLASH readout. Magn Reson Med 2010; 64: 439 â 446.
dc.identifier.citedreferenceJajamovich GH, Calcagno C, Dyvorne HA, Rusinek H, Taouli B. DCEâ MRI of the liver: reconstruction of the arterial input function using a low dose preâ bolus contrast injection. PLoS One 2014; 9: e115667.
dc.identifier.citedreferenceKoh TS, Thng CH, Hartono S, et al. Dynamic contrastâ enhanced MRI of neuroendocrine hepatic metastases: A feasibility study using a dualâ input twoâ compartment model. Magn Reson Med 2011; 65: 250 â 260.
dc.identifier.citedreferenceLe Y, Dale B, Akisik F, Koons K, Lin C. Improved T1, contrast concentration, and pharmacokinetic parameter quantification in the presence of fat with twoâ point Dixon for dynamic contrastâ enhanced magnetic resonance imaging. Magn Reson Med 2016; 75: 1677 â 1684.
dc.identifier.citedreferenceSacolick LI, Wiesinger F, Hancu I, Vogel MW. B1 mapping by Blochâ Siegert shift. Magn Reson Med 2010; 63: 1315 â 1322.
dc.identifier.citedreferenceVoigt T, Nehrke K, Doessel O, Katscher U. T1 corrected B1 mapping using multiâ TR gradient echo sequences. Magn Reson Med 2010; 64: 725 â 733.
dc.identifier.citedreferenceWang H, Cao Y. Correction of arterial input function in dynamic contrastâ enhanced MRI of the liver. J Magn Reson Imaging 2012; 36: 411 â 421.
dc.identifier.citedreferenceGriffith B, Jain R. Perfusion imaging in neuroâ oncology: basic techniques and clinical applications. Magn Reson Imaging Clin N Am 2016; 24: 765 â 779.
dc.identifier.citedreferenceMazaheri Y, Akin O, Hricak H. Dynamic contrastâ enhanced magnetic resonance imaging of prostate cancer: A review of current methods and applications. World J Radiol 2017; 9: 416 â 425.
dc.identifier.citedreferencePinker K, Helbich TH, Morris EA. The potential of multiparametric MRI of the breast. Br J Radiol 2017; 90: 20160715.
dc.identifier.citedreferenceWibmer AG, Sala E, Hricak H, Vargas HA. The expanding landscape of diffusionâ weighted MRI in prostate cancer. Abdom Radiol 2016; 41: 854 â 861.
dc.identifier.citedreferenceYang X, Knopp MV. Quantifying tumor vascular heterogeneity with dynamic contrastâ enhanced magnetic resonance imaging: a review. J Biomed Biotechnol 2011; 2011: 732848.
dc.identifier.citedreferenceYang X, Liang J, Heverhagen JT, et al. Improving the pharmacokinetic parameter measurement in dynamic contrastâ enhanced MRI by use of the arterial input function: theory and clinical application. Magn Reson Med 2008; 59: 1448 â 1456.
dc.identifier.citedreferenceObuchowski NA, Buckler A, Kinahan P, et al. Statistical issues in testing conformance with the quantitative imaging biomarker alliance (QIBA) profile claims. Acad Radiol 2016; 23: 496 â 506.
dc.identifier.citedreferenceMa D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature 2013; 495: 187 â 192.
dc.identifier.citedreferenceChen Y, Jiang Y, Pahwa S, et al. MR fingerprinting for rapid quantitative abdominal imaging. Radiology 2016; 279: 278 â 286.
dc.identifier.citedreferencePadhani AR, Liu G, Koh DM, et al. Diffusionâ weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 2009; 11: 102 â 125.
dc.identifier.citedreferenceTofts P. Quantitative MRI of the brain: measuring changes caused by disease. John Wiley & Sons Ltd, Chichester, UK. 2003.
dc.identifier.citedreferenceChenevert TL, Ross BD. Diffusion imaging for therapy response assessment of brain tumor. Neuroimaging Clin N Am 2009; 19: 559 â 571.
dc.identifier.citedreferencePadhani AR, Khan AA. Diffusionâ weighted (DW) and dynamic contrastâ enhanced (DCE) magnetic resonance imaging (MRI) for monitoring anticancer therapy. Target Oncol 2010; 5: 39 â 52.
dc.identifier.citedreferenceBarnes A, Alonzi R, Blackledge M, et al. UK quantitative WBâ DWI technical workgroup: consensus meeting recommendations on optimisation, quality |control, processing and analysis of quantitative wholeâ body diffusionâ weighted imaging for cancer. Br J Radiol 2018; 91: 20170577.
dc.identifier.citedreferenceBeuzit L, Eliat PA, Brun V, et al. Dynamic contrastâ enhanced MRI: Study of interâ software accuracy and reproducibility using simulated and clinical data. J Magn Reson Imaging 2016; 43: 1288 â 1300.
dc.identifier.citedreferenceCosta DN, Pedrosa I, Roehrborn C, Rofsky NM. Multiparametric magnetic resonance imaging of the prostate: technical aspects and role in clinical management. Top Magn Reson Imaging 2014; 23: 243 â 257.
dc.identifier.citedreferencePartridge SC, Nissan N, Rahbar H, Kitsch AE, Sigmund EE. Diffusionâ weighted breast MRI: Clinical applications and emerging techniques. J Magn Reson Imaging 2017; 45: 337 â 355.
dc.identifier.citedreferenceWinfield JM, Payne GS, Weller A, deSouza NM. DCEâ MRI, DWâ MRI, and MRS in cancer: Challenges and advantages of implementing qualitative and quantitative multiâ parametric imaging in the clinic. Top Magn Reson Imaging 2016; 25: 245 â 254.
dc.identifier.citedreferenceJansen JF, Parra C, Lu Y, Shuklaâ Dave A. Evaluation of head and neck tumors with functional MR imaging. Magn Reson Imaging Clin N Am 2016; 24: 123 â 133.
dc.identifier.citedreferenceLi SP, Padhani AR. Tumor response assessments with diffusion and perfusion MRI. J Magn Reson Imaging 2012; 35: 745 â 763.
dc.identifier.citedreferenceShuklaâ Dave A, Hricak H. Role of MRI in prostate cancer detection. NMR Biomed 2014; 27: 16 â 24.
dc.identifier.citedreferenceGalban CJ, Hoff BA, Chenevert TL, Ross BD. Diffusion MRI in early cancer therapeutic response assessment. NMR Biomed 2017; 30 ( 3 ).
dc.identifier.citedreferenceTofts PS, Brix G, Buckley DL, et al. Estimating kinetic parameters from dynamic contrastâ enhanced T(1)â weighted MRI of a diffusable tracer: Standardized quantities and symbols. J Magn Reson Imaging 1999; 10: 223 â 232.
dc.identifier.citedreferenceThoeny HC, Ross BD. Predicting and monitoring cancer treatment response with diffusionâ weighted MRI. J Magn Reson Imaging 2010; 32: 2 â 16.
dc.identifier.citedreferenceLeach MO, Brindle KM, Evelhoch JL, et al. The assessment of antiangiogenic and antivascular therapies in earlyâ stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer 2005; 92: 1599 â 1610.
dc.identifier.citedreferenceKoh DM, Padhani AR. Functional magnetic resonance imaging of the liver: parametric assessments beyond morphology. Magn Reson Imaging Clin N Am 2010; 18: 565 â 585.
dc.identifier.citedreferenceO’Connor JP, Aboagye EO, Adams JE, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 2017; 14: 169 â 186.
dc.identifier.citedreferenceKim MM, Parolia A, Dunphy MP, Venneti S. Nonâ invasive metabolic imaging of brain tumours in the era of precision medicine. Nat Rev Clin Oncol 2016; 13: 725 â 739.
dc.identifier.citedreferenceWong KH, Panek R, Bhide SA, Nutting CM, Harrington KJ, Newbold KL. The emerging potential of magnetic resonance imaging in personalizing radiotherapy for head and neck cancer: an oncologist’s perspective. Br J Radiol 2017; 90: 20160768.
dc.identifier.citedreferenceKessler LG, Barnhart HX, Buckler AJ, et al. The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat Methods Med Res 2015; 24: 9 â 26.
dc.identifier.citedreferenceNg CS, Raunig DL, Jackson EF, et al. Reproducibility of perfusion parameters in dynamic contrastâ enhanced MRI of lung and liver tumors: effect on estimates of patient sample size in clinical trials and on individual patient responses. AJR Am J Roentgenol 2010; 194: W134 â 140.
dc.identifier.citedreferenceWeller A, Papoutsaki MV, Waterton JC, et al. Diffusionâ weighted (DW) MRI in lung cancers: ADC testâ retest repeatability. Eur Radiol 2017; 27: 4552 â 4562.
dc.identifier.citedreferenceMaclaren J, Han Z, Vos SB, Fischbein N, Bammer R. Reliability of brain volume measurements: a testâ retest dataset. Sci Data 2014; 1: 140037.
dc.identifier.citedreferenceYokoo T, Serai SD, Pirasteh A, et al. Linearity, bias, and precision of hepatic proton density fat fraction measurements by using MR imaging: A metaâ analysis. Radiology 2018; 286: 486 â 498.
dc.identifier.citedreferencePetersen ET, Mouridsen K, Golay X, all named coâ authors of the Qtâ rs. The QUASAR reproducibility study, Part II: Results from a multiâ center Arterial Spin Labeling testâ retest study. NeuroImage 2010; 49: 104 â 113.
dc.identifier.citedreferenceHectors SJ, Wagner M, Corcueraâ Solano I, et al. Comparison between 3â scan trace and diagonal body diffusionâ weighted imaging acquisitions: a phantom and volunteer study. Tomography 2016; 2: 411 â 420.
dc.identifier.citedreferenceMalyarenko D, Galban CJ, Londy FJ, et al. Multiâ system repeatability and reproducibility of apparent diffusion coefficient measurement using an iceâ water phantom. J Magn Reson Imaging 2013; 37: 1238 â 1246.
dc.identifier.citedreferenceRaunig DL, McShane LM, Pennello G, et al. Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment. Stat Methods Med Res 2015; 24: 27 â 67.
dc.identifier.citedreferenceBonekamp D, Nagae LM, Degaonkar M, et al. Diffusion tensor imaging in children and adolescents: reproducibility, hemispheric, and ageâ related differences. NeuroImage 2007; 34: 733 â 742.
dc.identifier.citedreferencePaldino MJ, Barboriak D, Desjardins A, Friedman HS, Vredenburgh JJ. Repeatability of quantitative parameters derived from diffusion tensor imaging in patients with glioblastoma multiforme. J Magn Reson Imaging 2009; 29: 1199 â 1205.
dc.identifier.citedreferencePfefferbaum A, Adalsteinsson E, Sullivan EV. Replicability of diffusion tensor imaging measurements of fractional anisotropy and trace in brain. J Magn Reson Imaging 2003; 18: 427 â 433.
dc.identifier.citedreferenceBraithwaite AC, Dale BM, Boll DT, Merkle EM. Shortâ and midterm reproducibility of apparent diffusion coefficient measurements at 3.0â T diffusionâ weighted imaging of the abdomen. Radiology 2009; 250: 459 â 465.
dc.identifier.citedreferenceDeckers F, De Foer B, Van Mieghem F, et al. Apparent diffusion coefficient measurements as very early predictive markers of response to chemotherapy in hepatic metastasis: a preliminary investigation of reproducibility and diagnostic value. J Magn Reson Imaging 2014; 40: 448 â 456.
dc.identifier.citedreferenceHeijmen L, Ter Voert EE, Nagtegaal ID, et al. Diffusionâ weighted MR imaging in liver metastases of colorectal cancer: reproducibility and biological validation. Eur Radiol 2013; 23: 748 â 756.
dc.identifier.citedreferenceMiquel ME, Scott AD, Macdougall ND, Boubertakh R, Bharwani N, Rockall AG. In vitro and in vivo repeatability of abdominal diffusionâ weighted MRI. Br J Radiol 2012; 85: 1507 â 1512.
dc.identifier.citedreferenceGibbs P, Pickles MD, Turnbull LW. Repeatability of echoâ planarâ based diffusion measurements of the human prostate at 3â T. Magn Reson Imaging 2007; 25: 1423 â 1429.
dc.identifier.citedreferenceJambor I, Merisaari H, Aronen HJ, et al. Optimization of bâ value distribution for biexponential diffusionâ weighted MR imaging of normal prostate. J Magn Reson Imaging 2014; 39: 1213 â 1222.
dc.identifier.citedreferenceJambor I, Merisaari H, Taimen P, et al. Evaluation of different mathematical models for diffusionâ weighted imaging of normal prostate and prostate cancer using high bâ values: a repeatability study. Magn Reson Med 2015; 73: 1988 â 1998.
dc.identifier.citedreferenceLitjens GJ, Hambrock T, Hulsbergenâ van de Kaa C, Barentsz JO, Huisman HJ. Interpatient variation in normal peripheral zone apparent diffusion coefficient: effect on the prediction of prostate cancer aggressiveness. Radiology 2012; 265: 260 â 266.
dc.identifier.citedreferenceAlonzi R, Taylor NJ, Stirling JJ, et al. Reproducibility and correlation between quantitative and semiquantitative dynamic and intrinsic susceptibilityâ weighted MRI parameters in the benign and malignant human prostate. J Magn Reson Imaging 2010; 32: 155 â 164.
dc.identifier.citedreferenceJackson A, Jayson GC, Li KL, et al. Reproducibility of quantitative dynamic contrastâ enhanced MRI in newly presenting glioma. Br J Radiol 2003; 76: 153 â 162.
dc.identifier.citedreferenceSullivan DC, Obuchowski NA, Kessler LG, et al. Metrology standards for quantitative imaging biomarkers. Radiology 2015; 277: 813 â 825.
dc.identifier.citedreferenceObuchowski NA, Bullen J. Quantitative imaging biomarkers: Effect of sample size and bias on confidence interval coverage. Stat Methods Med Res 2017:962280217693662.
dc.identifier.citedreferenceHuang EP, Wang XF, Choudhury KR, et al. Metaâ analysis of the technical performance of an imaging procedure: guidelines and statistical methodology. Stat Methods Med Res 2015; 24: 141 â 174.
dc.identifier.citedreferenceLe Bihan D. Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. Radiology 2013; 268: 318 â 322.
dc.identifier.citedreferenceLe Bihan D, Delannoy J, Levin RL. Temperature mapping with MR imaging of molecular diffusion: application to hyperthermia. Radiology 1989; 171: 853 â 857.
dc.identifier.citedreferenceHolz M, Heil SR, Sacco A. Temperatureâ dependent selfâ diffusion coefficients of water and six selected molecular liquids for calibration in accurate Hâ 1â NMR PFG measurements. Phys Chem Chem Phys 2000; 2: 4740 â 4742.
dc.identifier.citedreferenceJerome NP, Papoutsaki MV, Orton MR, et al. Development of a temperatureâ controlled phantom for magnetic resonance quality assurance of diffusion, dynamic, and relaxometry measurements. Med Phys 2016; 43: 2998 â 3007.
dc.identifier.citedreferenceNewitt DC, Malyarenko D, Chenevert TL, et al. Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. J Med Imaging 2018; 5: 011003.
dc.identifier.citedreferenceChenevert TL, Galban CJ, Ivancevic MK, et al. Diffusion coefficient measurement using a temperatureâ controlled fluid for quality control in multicenter studies. J Magn Reson Imaging 2011; 34: 983 â 987.
dc.identifier.citedreferenceBharwani N, Koh DM. Diffusionâ weighted imaging of the liver: an update. Cancer Imaging 2013; 13: 171 â 185.
dc.identifier.citedreferenceChenevert TL, Stegman LD, Taylor JM, et al. Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst 2000; 92: 2029 â 2036.
dc.identifier.citedreferenceJakubovic R, Zhou S, Heyn C, et al. The predictive capacity of apparent diffusion coefficient (ADC) in response assessment of brain metastases following radiation. Clin Exp Metastasis 2016; 33: 277 â 284.
dc.identifier.citedreferenceJansen JF, Schoder H, Lee NY, et al. Tumor metabolism and perfusion in head and neck squamous cell carcinoma: pretreatment multimodality imaging with 1H magnetic resonance spectroscopy, dynamic contrastâ enhanced MRI, and [18F]FDGâ PET. Int J Radiat Oncol Biol Phys 2012; 82: 299 â 307.
dc.identifier.citedreferencePartridge SC, Singer L, Sun R, et al. Diffusionâ weighted MRI: influence of intravoxel fat signal and breast density on breast tumor conspicuity and apparent diffusion coefficient measurements. Magn Reson Imaging 2011; 29: 1215 â 1221.
dc.identifier.citedreferenceMulkern RV, Ricci KI, Vajapeyam S, et al. Pediatric brain tumor consortium multisite assessment of apparent diffusion coefficient zâ axis variation assessed with an iceâ water phantom. Acad Radiol 2015; 22: 363 â 369.
dc.identifier.citedreferenceNewitt DC, Tan ET, Wilmes LJ, et al. Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial. J Magn Reson Imaging 2015; 42: 908 â 919.
dc.identifier.citedreferenceJafar MM, Parsai A, Miquel ME. Diffusionâ weighted magnetic resonance imaging in cancer: Reported apparent diffusion coefficients, inâ vitro and inâ vivo reproducibility. World J Radiol 2016; 8: 21 â 49.
dc.identifier.citedreferenceTaouli B, Beer AJ, Chenevert T, et al. Diffusionâ weighted imaging outside the brain: Consensus statement from an ISMRMâ sponsored workshop. J Magn Reson Imaging 2016; 44: 521 â 540.
dc.identifier.citedreferenceBoss MA. Multicenter study of reproducibility of wide range of ADC at 0°C. Chicago: RSNA; 2015.
dc.identifier.citedreferencePalacios EM, Martin AJ, Boss MA, et al. Toward precision and reproducibility of diffusion tensor imaging: a multicenter diffusion phantom and traveling volunteer study. AJNR Am J Neuroradiol 2017; 38: 537 â 545.
dc.identifier.citedreferencePierpaoli C, Joelle SUN, Basser PJ, Horkary F. Polyvinylpyrrolidone (PVP) water solutions as isotropic phantoms for diffusion MRI studies. In: Proc 17th Annual Meeting ISMRM, Honolulu; 2009.
dc.identifier.citedreferenceNCI Recommendations for MR measurement methods at 1.5 Tesla and endpoints for use in Phase 1/2a trials of antiâ cancer therapuetics affecting tumor vascular function. Dynamic contrast MRI (DCEâ MRI) guidelines resulted from the NCI CIP MRI workshop on Translational Research Center in Cancer. MR Workshop on Translational Research; 2004.
dc.identifier.citedreferenceTaylor JS, Tofts PS, Port R, et al. MR imaging of tumor microcirculation: promise for the new millennium. J Magn Reson Imaging 1999; 10: 903 â 907.
dc.identifier.citedreferenceLeach MO, Brindle KM, Evelhoch JL, et al. The assessment of antiangiogenic and antivascular therapies in earlyâ stage clinical trials using magnetic resonance imaging: issues and recommendations. Br J Cancer 2005; 92: 1599 â 1610.
dc.identifier.citedreferenceLeach MO, Morgan B, Tofts PS, et al. Imaging vascular function for early stage clinical trials using dynamic contrastâ enhanced magnetic resonance imaging. Eur Radiol 2012; 22: 1451 â 1464.
dc.identifier.citedreferenceJensen LR, Garzon B, Heldahl MG, Bathen TF, Lundgren S, Gribbestad IS. Diffusionâ weighted and dynamic contrastâ enhanced MRI in evaluation of early treatment effects during neoadjuvant chemotherapy in breast cancer patients. J Magn Reson Imaging 2011; 34: 1099 â 1109.
dc.identifier.citedreferenceRosen M, Kinahan PE, Gimpel JF, et al. Performance observations of scanner qualification of NCIâ designated cancer centers: results from the Centers of Quantitative Imaging Excellence (CQIE) Program. Acad Radiol 2017; 24: 232 â 245.
dc.identifier.citedreferenceBane O, Hectors SJ, Wagner M, et al. Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCEâ MRI: Results from a multicenter phantom study. Magn Reson Med 2018; 79: 2564 â 2575.
dc.identifier.citedreferenceKim H, Mousa M, Schexnailder P, et al. Portable perfusion phantom for quantitative DCEâ MRI of the abdomen. Med Phys 2017; 44: 5198 â 5209.
dc.identifier.citedreferenceDriscoll B, Keller H, Coolens C. Development of a dynamic flow imaging phantom for dynamic contrastâ enhanced CT. Med Phys 2011; 38: 4866 â 4880.
dc.identifier.citedreferenceSorace AG, Wu C, Barnes SL, et al. Repeatability, reproducibility, and accuracy of quantitative MRI of the breast in the community radiology setting. J Magn Reson Imaging 2018 [Epub ahead of print].
dc.identifier.citedreferenceKim S, Loevner L, Quon H, et al. Diffusionâ weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck. Clin Cancer Res 2009; 15: 986 â 994.
dc.identifier.citedreferenceBagherâ Ebadian H, Jain R, Nejadâ Davarani SP, et al. Model selection for DCEâ T1 studies in glioblastoma. Magn Reson Med 2012; 68: 241 â 251.
dc.identifier.citedreferenceHambrock T, Somford DM, Huisman HJ, et al. Relationship between apparent diffusion coefficients at 3.0â T MR imaging and Gleason grade in peripheral zone prostate cancer. Radiology 2011; 259: 453 â 461.
dc.identifier.citedreferenceKim JH, Kim JK, Park BW, Kim N, Cho KS. Apparent diffusion coefficient: prostate cancer versus noncancerous tissue according to anatomical region. J Magn Reson Imaging 2008; 28: 1173 â 1179.
dc.identifier.citedreferenceOto A, Yang C, Kayhan A, et al. Diffusionâ weighted and dynamic contrastâ enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis. AJR Am J Roentgenol 2011; 197: 1382 â 1390.
dc.identifier.citedreferencePartridge SC, Stone KM, Strigel RM, DeMartini WB, Peacock S, Lehman CD. Breast DCEâ MRI: influence of postcontrast timing on automated lesion kinetics assessments and discrimination of benign and malignant lesions. Acad Radiol 2014; 21: 1195 â 1203.
dc.identifier.citedreferenceTaouli B, Koh DM. Diffusionâ weighted MR imaging of the liver. Radiology 2010; 254: 47 â 66.
dc.identifier.citedreferenceLin X, Lee M, Buck O, et al. Diagnostic accuracy of T1â weighted dynamic contrastâ enhancedâ MRI and DWIâ ADC for differentiation of glioblastoma and primary CNS lymphoma. AJNR Am J Neuroradiol 2017; 38: 485 â 491.
dc.identifier.citedreferenceVandecaveye V, Dirix P, De Keyzer F, et al. Diffusionâ weighted magnetic resonance imaging early after chemoradiotherapy to monitor treatment response in headâ andâ neck squamous cell carcinoma. Int J Radiat Oncol Biol Phys 2012; 82: 1098 â 1107.
dc.identifier.citedreferenceVandecaveye V, Dirix P, De Keyzer F, et al. Diffusionâ weighted magnetic resonance imaging early after chemoradiotherapy to monitor treatment response in headâ andâ neck squamous cell carcinoma. Int J Radiat Oncol Biol Phys 2012; 82: 1098 â 1107.
dc.identifier.citedreferenceDonati OF, Afaq A, Vargas HA, et al. Prostate MRI: evaluating tumor volume and apparent diffusion coefficient as surrogate biomarkers for predicting tumor Gleason score. Clin Cancer Res 2014; 20: 3705 â 3711.
dc.identifier.citedreferenceDonati OF, Mazaheri Y, Afaq A, et al. Prostate cancer aggressiveness: assessment with wholeâ lesion histogram analysis of the apparent diffusion coefficient. Radiology 2014; 271: 143 â 152.
dc.identifier.citedreferenceKorn N, Kurhanewicz J, Banerjee S, Starobinets O, Saritas E, Noworolski S. Reducedâ FOV excitation decreases susceptibility artifact in diffusionâ weighted MRI with endorectal coil for prostate cancer detection. Magn Reson Imaging 2015; 33: 56 â 62.
dc.identifier.citedreferenceBanerjee S, Nishimura DG, Shankaranarayanan A, Saritas EU. Reduced fieldâ ofâ view DWI with robust fat suppression and unrestricted slice coverage using tilted 2D RF excitation. Magn Reson Med 2016; 76: 1668 â 1676.
dc.identifier.citedreferenceChen NK, Guidon A, Chang HC, Song AW. A robust multiâ shot scan strategy for highâ resolution diffusion weighted MRI enabled by multiplexed sensitivityâ encoding (MUSE). NeuroImage 2013; 72: 41 â 47.
dc.identifier.citedreferenceHirokawa Y, Isoda H, Maetani YS, Arizono S, Shimada K, Togashi K. MRI artifact reduction and quality improvement in the upper abdomen with PROPELLER and prospective acquisition correction (PACE) technique. AJR Am J Roentgenol 2008; 191: 1154 â 1158.
dc.identifier.citedreferencePipe JG, Farthing VG, Forbes KP. Multishot diffusionâ weighted FSE using PROPELLER MRI. Magn Reson Med 2002; 47: 42 â 52.
dc.identifier.citedreferenceMalkyarenko DI, Chenevert TL. Practical estimate of gradient nonlinearity for implementation of apparent diffusion coefficient bias correction. J Magn Reson Imaging 2014; 40: 1487 â 1495.
dc.identifier.citedreferenceMills SJ, Soh C, Rose CJ, et al. Candidate biomarkers of extravascular extracellular space: a direct comparison of apparent diffusion coefficient and dynamic contrastâ enhanced MR imagingâ derived measurement of the volume of the extravascular extracellular space in glioblastoma multiforme. AJNR Am J Neuroradiol 2010; 31: 549 â 553.
dc.identifier.citedreferenceYankeelov TE, Lepage M, Chakravarthy A, et al. Integration of quantitative DCEâ MRI and ADC mapping to monitor treatment response in human breast cancer: initial results. Magn Reson Imaging 2007; 25: 1 â 13.
dc.identifier.citedreferenceVandecaveye V, De Keyzer F, Nuyts S, et al. Detection of head and neck squamous cell carcinoma with diffusion weighted MRI after (chemo)radiotherapy: Correlation between radiologic and histopathologic findings. Int J Radiat Oncol Biol Phys 2007; 67: 960 â 971.
dc.identifier.citedreferenceChandarana H, Taouli B. Diffusion and perfusion imaging of the liver. Eur J Radiol 2010; 76: 348 â 358.
dc.identifier.citedreferenceKanda T, Oba H, Toyoda K, Kitajima K, Furui S. Brain gadolinium deposition after administration of gadoliniumâ based contrast agents. Jpn J Radiol 2016; 34: 3 â 9.
dc.identifier.citedreferenceTofts PS. Modeling tracer kinetics in dynamic Gdâ DTPA MR imaging. J Magn Reson Imaging 1997; 7: 91 â 101.
dc.identifier.citedreferenceChen J, Yao J, Thomasson D. Automatic determination of arterial input function for dynamic contrast enhanced MRI in tumor assessment. Med Image Comput Comput Assist Interv 2008; 11 ( Pt 1 ): 594 â 601.
dc.identifier.citedreferenceLi X, Welch EB, Arlinghaus LR, et al. A novel AIF tracking method and comparison of DCEâ MRI parameters using individual and populationâ based AIFs in human breast cancer. Phys Med Biol 2011; 56: 5753 â 5769.
dc.identifier.citedreferenceParker GJ, Roberts C, Macdonald A, et al. Experimentallyâ derived functional form for a populationâ averaged highâ temporalâ resolution arterial input function for dynamic contrastâ enhanced MRI. Magn Reson Med 2006; 56: 993 â 1000.
dc.identifier.citedreferenceWang S, Fan X, Medved M, et al. Arterial input functions (AIFs) measured directly from arteries with low and standard doses of contrast agent, and AIFs derived from reference tissues. Magn Reson Imaging 2016; 34: 197 â 203.
dc.identifier.citedreferenceSchnall MD, Blume J, Bluemke DA, et al. Diagnostic architectural and dynamic features at breast MR imaging: Multicenter study. Radiology 2006; 238: 42 â 53.
dc.identifier.citedreferenceD’Orsi CJ SE, Mendelson EB, Morris EA, et al. ACR BIâ RADS® Atlas, Breast Imaging Reporting and Data System. Reston, VA: American College of Radiology, 2013.
dc.identifier.citedreferenceHylton NM, Blume JD, Bernreuter WK, et al. Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapyâ results from ACRIN 6657/Iâ SPY TRIAL. Radiology 2012; 263: 663 â 672.
dc.identifier.citedreferenceHylton NM, Gatsonis CA, Rosen MA, et al. Neoadjuvant chemotherapy for breast cancer: functional tumor volume by MR imaging predicts recurrenceâ free survivalâ results from the ACRIN 6657/CALGB 150007 Iâ SPY 1 TRIAL. Radiology 2016; 279: 44 â 55.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
jmri26518_am.pdf
Size:
3.166MB
Format:
PDF
View/Open
Name:
jmri26518.pdf
Size:
4.046MB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.