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Last Updated: 11/19/19
Quantitative Imaging Network (QIN)

Selected QIN Publications

Since the inception of the QIN program nearly a decade ago, QIN team members have actively published nearly 400 peer reviewed articles in various imaging journals. A profile of recent published articles by specific QIN team can be found under their respective team's name. A sample of a few recent representative QIN articles are listed here below:

  1. Columbia
    Yang H, Schwartz LH, and Zhao B. A Response Assessment Platform for Development and Validation of Imaging Biomarkers in Oncology. Tomography. 2016; 2(4):406-410.

  2. Emory
    Gurbani SS, Schreibmann E, Maudsley AA, Cordova JS, Soher BJ, Poptani H, Verma G, Barker PB, Shim H, Cooper LAD (2018) A Convolutional Neural Network to Filter Artifacts in spectroscopic MRI. Magn Reson Med 80(5): 1765-1775. Epub March 9, 2018.

  3. John Hopkins
    Jha AK, Mena E, Caffo B, Ashrafinia S, Rahmim A, Frey EC and Subramaniam R, “A practical no-gold-standard framework to evaluate quantitative imaging methods: Application to lesion segmentation in PET”, J. Med. Imag., Special Section on PET imaging, 4(1), 011011, PMCID: PMC5335899, 2017.

  4. Stanford
    J. Wu, G. Cao, X. Sun, J. Lee, D.L. Rubin, S. Napel, A.W. Kurian, B. Daniel, R. Li, “Intratumoral spatial heterogeneity by perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy,” Radiology 288(1):26-35, 2018 doi: 10.1148/radiol.2018172462

  5. U. Washington 
    Kurland BF, Muzi M, Peterson LM, Doot RK, Wangerin KA, Mankoff DA, Linden HM, Kinahan PE. Multicenter Clinical Trials Using 18F-FDG PET to Measure Early Response to Oncologic Therapy: Effects of Injection-to-Acquisition Time Variability on Required Sample Size. J Nucl Med 57:226-230, 2016. PMID: 26493206.PMCID: PMC4749350

  6. UCLA
    Young S, Lo P, Kim G, Brown M, Hoffman J, Hsu W, Wahi-Anwar W, Flores C, Lee G, Noo F, Goldin J, McNitt-Gray M. The effect of radiation dose reduction on computer-aided detection (CAD) performance in a low-dose lung cancer screening population. Med Phys. 2017 Apr;44(4):1337-1346. PMID: 28122122

  7. Univ. Iowa
    Beichel RR, Van Tol M, Ulrich EJ, Bauer C, Chang T, Plichta KA, Smith BJ, Sunderland JJ, Graham MM, Sonka M, Buatti JM. Semi-automated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach. Med Phys. Jun 2016; 43(6):2948. PMID: 27277044.

  8. Mayo
    Machine Learning: Discovering the Future of Medical Imaging. Erickson BJ. J Digit Imaging. 2017 Aug; 30(4):391. PMID: 28653122

  9. U. Penn
    Scheuermann JS, et al. J Nucl Med. 58(7):1065-1071. 7 2017. doi: 10.2967/jnumed.116.186759. Epub 2017 Mar 2. Qualification of National Cancer Institute-Designated Cancer Centers for Quantitative PET/CT Imaging in Clinical Trials. PMID: 28254874

  10. Dana Farber Cancer Institute
    Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL, “Artificial intelligence in radiology“, Nature Reviews Cancer, 2018 Aug;18(8):500-510.

A few recent QIN collaborative publications:

Press RH, Shu HG, Shim H, Mountz JM, Kurland BF, Wahl RL, Jones EF, Hylton NM, Gerstner ER, Nordstrom, RJ, Henderson L, Kurdziel KA, Vikram B, Jacobs MA, Holdhoff M, Taylor E, Jaffray DA, Schwartz LH, Mankoff DA, Kinahan PE, Linden HM, Lambin P, Dilling TJ, Rubin DL, Hadjiiski L, Buatti JM (2018) The use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective. Int J Radiat Oncol Biol Phys, In Press. Available online June 30, 2018. PMID: 29966725

M. Zhou, J. Scott, B. Chaudhury, L. Hall, D. Goldgof, K. W. Yeom, M. Iv, Y. Ou, J. Kalpathy-Cramer, S. Napel, R. Gillies, O. Gevaert, R. Gatenby, “Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors and Machine-learning Approaches,” AJNR Am J Neuroradiol. 2018 39(2):2018-216 doi: 10.3174/ajnr.A5391. [Epub ahead of print] PMID:28982791. PMCID: PMC5749594.

Balagurunathan Y, Beers A, Kalpathy-Cramer J, McNitt-Gray M, Hadjiiski L, Zhao B, Zhu J, Yang H, Yip SSF, Aerts HJWL, Napel S, Cherezov D, Cha K, Chan HP, Flores C, Garcia A, Gillies R, Goldgof D. “Semi-automated pulmonary nodule interval segmentation using the NLST data.” Med Phys. 2018 Mar;45(3):1093-1107.

Recent QIN Team’s Annual Meeting Reports:

2019 Annual Report