The Cancer Imaging Archive (TCIA) Public Access

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Comment: optimizing info boxes with related publications prior to submission of Collections to TCIA

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titleThe following source material predate TCIA but refer to the LIDC Collection data, created before submission to TCIA
  1. Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, et al.:The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans.Medical Physics, 38: 915–931, 2011. (link)
  2. Armato SG III, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Reeves AP, Croft BY, Clarke LP, The Lung Image Database Consortium Research Group: Lung Image Database Consortium: Developing a resource for the medical imaging research community. Radiology 232: 739–748, 2004.
  3. Meyer CR, Johnson TD, McLennan G, Aberle DR, Kazerooni EA, MacMahon H, Mullan BF, Yankelevitz DF, van Beek EJR, Armato SG III, McNitt-Gray MF, Reeves AP, Gur D, Henschke CI, Hoffman EA, Bland PH, Laderach G, Pais R, Qing D, Piker C, Guo J, Starkey A, Max D, Croft BY, Clarke LP: Evaluation of lung MDCT nodule annotation across radiologists and methods. Academic Radiology 13: 1254–1265, 2006.
  4. Armato SG III, McNitt-Gray MF, Reeves AP, Meyer CR, McLennan G, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, Hoffman EA, Henschke CI, Roberts RY, Brown MS, Engelmann RM, Pais RC, Piker CW, Qing D, Kocherginsky M, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): An evaluation of radiologist variability in the identification of lung nodules on CT scans. Academic Radiology 14: 1409–1421, 2007.
  5. Armato SG III, Roberts RY, McNitt-Gray MF, Meyer CR, Reeves AP, McLennan G, Engelmann RM, Bland PH, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): Ensuring the integrity of expert-defined “truth.” Academic Radiology 14: 1455–1463, 2007.
  6. McNitt-Gray MF, Armato SG III, Meyer CR, Reeves AP, McLennan G, Pais R, Freymann J, Brown MS, Engelmann RM, Bland PH, Laderach GE, Piker C, Guo J, Towfic Z, Qing DP, Yankelevitz DF, Aberle DR, van Beek EJR, MacMahon H, Kazerooni EA, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation. Academic Radiology 14: 1464–1474, 2007.
  7. Reeves AP, Biancardi AM, Apanasovich TV, Meyer CR, MacMahon H, van Beek EJR, Kazerooni EA, Yankelevitz DF, McNitt-Gray MF, McLennan G, Armato SG III, Henschke CI, Aberle DR, Croft BY, Clarke LP: The Lung Image Database Consortium (LIDC): A comparison of different size metrics for pulmonary nodule measurements. Academic Radiology 14: 1475–1485, 2007.
  8. Armato SG III, Roberts RY, Kocherginsky M, Aberle DR, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz DF, McLennan G, McNitt-Gray MF, Meyer CR, Reeves AP, Caligiuri P, Quint LE, Sundaram B, Croft BY, Clarke LP: Assessment of radiologist performance in the detection of lung nodules: Dependence on the definition of “truth”. Academic Radiology 16: 28–38, 2009.

Collection: Mouse-Mammary

 

 

Info
titleThe following source material pre-date TCIA but These refer to the Mouse-Mammary Collection data, created before submission to TCIA
  1. Jansen SA et al, NMR Biomed. 2011 Aug;24(7):880-7. 
  2. Jansen SA et al, Breast Cancer Res. 2009;11(5):R65. 
  3. Jansen SA et al, Radiology. 2009 Nov;253(2):399-406.
  4. Jansen SA et al, Phys Med Biol. 2008 Oct 7;53(19):5481-93.
  5. Jansen SA., Ductile carcinoma in situ: magnetic resonance and ultrasound imaging in mouse models of breast cancer (Mouse.Mammary.MRI.Ultrasound.Summary.pdf).
  6. Jansen S., Investigating genetic events in the progression of ductal carcinoma in situ (Mouse.Mammary.Genetics.DCIS.pdf).

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  1. Li X, Abramson RG, Arlinghaus LR, Kang H, Chakravarthy AB, Abramson VG, Farley J, Mayer IA, Kelley MC, Meszoely IM, Means-Powell J, Grau AM, Sanders M, Yankeelov TE. Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer. Investigative Radiology, 2015 Apr;50(4):195-204. PMCID: PMC4471951 doi: 10.1097/RLI.0000000000000100.

  2. Weis JA, Miga MI, Arlinghaus LR, Li X, Abramson V, Chakravarthy AB, Pendyala P, Yankeelov TE. Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model. Cancer Res. 2015 Nov 15;75(22):4697-707. doi: 10.1158/0008-5472.CAN-14-2945.

  3. Atuegwu NC, Arlinghaus L, Li X, Welch EB, Chakravarthy AB, Gore JC, Yankeelov TE. Integration of diffusion weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy. Magnetic Resonance in Medicine 2011; 66:1689-96. PMCID: PMC3218213
  4. Li, X, Welch EB, Chakravarthy B, Mayer I, Meszeoly I, Kelley M, Means-Powell J, Gore JC, Yankeelov TE. Statistical comparison of dynamic contrast-enhanced MRI pharmacokinetic models in human breast cancer. Magnetic Resonance in Medicine, 2012; 68:261-71. PMCID: PMC3291742
  5. Smith DS, Gambrell JV, Li X, Arlinghaus LA, Quarles CC, Yankeelov TE, Welch EB. Robustness of Quantitative Compressive Sensing MRI: The Effect of Random Acquisitions on Derived Parameters for DCE and DSC-MRI. IEEE Transactions in Medical Imaging, 2012; 31:504-11. PMCID: PMC3289060
  6. Smith DS, Gore JC, Yankeelov TE, Welch EB. Real-time Compressive Sensing MRI Reconstruction using GPU Computing and Split Bregman Methods. International Journal of Biomedical Imaging, 2012; 2012:864827. PMCID: PMC3296267
  7. Dula AN, Arlinghaus LR, Dortch RD, Dewey BE, Whisenant JE, Ayers GD, Yankeelov TE, Smith SE. Amide Proton Transfer Imaging of the Breast at 3 T: Establishing reproducibility and possible feasibility for assessing chemotherapy response. Magnetic Resonance in Medicine, 2013; 70: 216-24. PMCID: PMC3505231
  8. Yankeelov TE, Peterson TE, Abramson RG, Garcia-Izquierdo D, Arlinghaus LR, Li X, Atuegwu NC, Catana C, Manning HC, Fayad ZA, Gore JC. Simultaneous PET-MRI in Oncology: A Solution Looking for a Problem? Magnetic Resonance Imaging, 2012; 30:1342-56. Selected as a Top 25 paper in Magnetic Resonance Imaging, 2012. PMCID: PMC3466373
  9. Abramson RG, Arlinghaus LR, Weis JA, Li X, Dula AN, Chekmenev EY, Smith SA, Miga MI, Abramson VG, Yankeelov TE. Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy. Breast Cancer: Targets and Therapies, 2012; 4: 139-154. PMCID: PMC3496377
  10. Li X, Abramson RG, Arlinghaus LR, Chakravarthy AB, Abramson V, Mayer I, Farley J, Delbeke D, Yankeelov TE. An Algorithm for Longitudinal Registration of PET/CT Images Acquired During Neoadjuvant Chemotherapy in Breast Cancer: Preliminary Results. European Journal of Nuclear Medicine and Molecular Imaging Research, 2012; 16:62. PMCID: PMC3520720
  11. Fluckiger U, Loveless ME, Barnes SL, Lepage M, Yankeelov TE. A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations, and experimental results. Physics in Medicine and Biology, 2013; 58:1983-98. PMCID: PMC3646091
  12. Yankeelov TE. Integrating Imaging Data into Predictive Biomathematical and Biophysical Models of Cancer. ISRN Biomathematics, 2012; Article ID 287394. PMCID: PMC3729405
  13. Atuegwu NC, Arlinghaus LR, Li X, Chakravarthy AB, Abramson VG, Sanders ME, Yankeelov TE. Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI Data to Predict Treatment Response and Changes in Breast Cancer Cellularity During Neoadjuvant Chemotherapy. Translational Oncology, 2013; 6:253-64. PMCID: PMC3660793
  14. Klomp DWJ, Dula AN, Arlinghaus LR, Italiaander M, Dortch RD, Zu Z, Williams JM, Gochberg DF, Luijten PR, Gore JC, Yankeelov TE, Smith SA. Amide Proton Transfer Imaging of the Human Breast at 7 Tesla: Development and Reproducibility. NMR in Biomedicine, 2013; 26:1271-7. PMCID: PMC3726578
  15. Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy AB, Abramson V, Bhave SR, Levy MA, Xu H, Yankeelov TE. Machine Learning for Predicting the Response of Breast Cancer to Neoadjuvant Chemotherapy. Journal of the American Medical Informatics Association, 2013; 20:688-95. PMCID: PMC3721158
  16. Li X, Arlinghaus LR, Ayers GD, Chakravarthy AB, Abramson RG, Abramson VG, Atuegwu N, Farley J, Mayer IA, Kelley MC, Meszoely IM, Means-Powell J, Grau AM, Sanders M, Bhave SR, Yankeelov TE. DCE-MRI Analysis Methods for Predicting the Response of Breast Cancer to Neoadjuvant Chemotherapy: Pilot Study Findings. Magnetic Resonance in Medicine, 2014; 71(4):1592-602. PMCID: PMC3742614
  17. Yankeelov TE, Atuegwu N, Hormuth D, Weis JA, Barnes SL, Miga MI, Rericha EC, Quaranta V. Clinically relevant modeling of tumor growth and treatment response. Science Translational Medicine 2013; 5:187ps9. PMCID: PMC3938952
  18. Abramson RG, Hoyt TL, Wilson KJ, Li X, Arlinghaus LR, Su P-F, Abramson VG, Chakravarthy AB, Yankeelov TE. Early Assessment of Breast Cancer Response to Neoadjuvant Chemotherapy by Semi- Quantitative Analysis of High Temporal Resolution DCE-MRI: Preliminary Results. Magnetic Resonance Imaging, 2013 ; 31:1457-64. PMCID: PMC3807825
  19. Weis JA, Miga MI, Arlinghaus LA, Li X, Chakravarthy AB, Abramson VG, Farley J, Yankeelov TE. A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy. Physics of Medicine and Biology, 2013; 58:5851-66. PMCID: PMC3791925
  20. Smith DA, Yankeelov TE, Welch EB. Potential of Compressed Sensing in Quantitative MR Imaging of Cancer. Cancer Imaging, 2013; 13:633-44. PMCID: PMC3893904
  21. Fluckiger JU, Li X, Whisenant JG, Peterson TE, Gore JC, Yankeelov TE. Using dynamic contrast enhanced magnetic resonance imaging data to constrain a positron emission tomography kinetic model: theory and simulations. International Journal of Biomedical Imaging, 2013; 2013:576470. PMCID: PMC3814089
  22. Fedorov A, Fluckiger J, Ayers GD, Li X, Gupta SN, Mulkern R, Yankeelov TE, Fennessy FM. A Comparison of Two Methods for Estimating DCE-MRI Parameters via Individual and Cohort Based AIFs in Prostate Cancer: A Step Towards Practical Implementation. Magnetic Resonance Imaging, 2014; 32:321-9. PMCID: PMC3965600
  23. Li X, Kang H, Arlinghaus LR, Abramson RG, Chakravarthy AB, Abramson VG, Farley J, Sanders M, Yankeelov TE. Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Translational Oncology, 2014; 7:14-22. PMCID: PMC3998687
  24. Chenevert TL, Malyarenko DI, Newitt D, Hylton N, Huang W, Li X, Tudorica A, Fedorov A, Fennessy F, Kikinis R, Arlinghaus L, Li X, Yankeelov TE, Muzi M, Marro KI, Kinahan PE, Jajamovich GH, Dyvorne HA, Taouli B, Kalpathy-Cramer J, Oborski MJ, Laymon CM, Mountz JM, Ross BD. Error in Quantitative Image Analysis Due to Platform-Dependent Image Scaling. Translational Oncology, 2014; 7:65-71. PMCID: PMC3998685
  25. Huang W, Li X, Chen Y, Li X, Chang M-C, Oborski MJ, Malyarenko DI, Muzi M, Jajamovich GH, Federov A, Tudorica A, Gupta S, Laymon CM, Marro KI, Dyvorne HA, Miller JV, Chenevert TL, Yankeelov TE, Mountz JM, Kinahan PE, Kikinis R, Taouli B, Fennessy F, Kalpathy-Cramer J. Variations of Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Evaluation of Breast Cancer Therapy Response: A Multicenter Data Analysis Challenge. Translational Oncology, 2014; 7:153-66. PMCID: PMC3998693
  26. Atuegwu NC, Li X, Arlinghaus LR, Abramson RG, Williams JM, Chakravarthy AB, Abramson V, Yankeelov TE. Longitudinal, Inter-modality Registration of Quantitative Breast PET and MRI Data Acquired Before and During Neoadjuvant Chemotherapy: Preliminary Results. Medical Physics, 2014; 41:052302. PMCID: PMC4000383
Info
titleThe following source material predate TCIA but These refer to the QIN-Breast Collection data, created before submission to TCIA
  1. Li X, Dawant BM, Welch EB, Chakravarthy AB, Freehardt D, Mayer I, Kelley M, Meszoely I, Gore JC, Yankeelov TE. Validation of an algorithm for the nonrigid registration of longitudinal breast MR images using realistic phantoms. Medical Physics, 2010; 37:2541-52. PMCID: PMC2881925
  2. Atuegwa NC, Gore JC, Yankeelov TE. Using Quantitative Imaging Data to Drive Mathematical Models of Tumor Growth and Treatment Response. Physics in Medicine and Biology, 2010; 55:2429-49. PMCID: PMC2897238
  3. Yankeelov TE, Arlinghaus L, Li X, Gore JC. The role of magnetic resonance imaging biomarkers in clinical trials of treatment response in cancer. Seminars in Oncology, 2011; 38:16-25. PMCID: PMC3073543
  4. Arlinghaus L, Li X, Levy M, Smith D, Welch WB, Gore JC, Yankeelov TE. Current and Future Trends in Magnetic Resonance Imaging Assessments of the Response of Breast Tumors to Neoadjuvant Chemotherapy. Journal of Oncology, 2010. pii: 919620. Epub 2010 Sep 29. PMCID: PMC2952974
  5. Arlinghaus LR, Welch EB, Chakravarthy AB, Farley JS, Gore JC, Yankeelov TE. Motion and distortion correction in diffusion-weighted MRI of the breast at 3T. Journal of Magnetic Resonance Imaging, 2011; 33:1063-70. PMCID: PMC3081111
  6. Gore JC, Manning HC, Quarles CC, Waddell KW, Yankeelov TE. Magnetic Resonance in the Era of Molecular Imaging of Cancer. Magnetic Resonance Imaging, 2011; 29:587-600. PMCID: PMC3285504
  7. Arlinghaus LR, Li X, Rahman AR, Welch EB, Xu L, Gore JC, Yankeelov TE. On the Relationship Between the Apparent Diffusion Coefficient and Extravascular Extracellular Volume Fraction in Human Breast Cancer. Magnetic Resonance Imaging, 2011; 29:630-8. PMCID: PMC3100356
  8. Smith DS, Welch EB, Li X, Arlinghaus LD, Loveless ME, Koyama T, Gore JC, Yankeelov TE. Quantitative effects of accelerated dynamic contrast enhanced MRI data using compressed sensing. Physics in Medicine and Biology, 2011; 56:4933-46. PMCID: PMC3192434
  9. Li, X, Welch EB, Chakravarthy B, Mayer I, Meszeoly I, Kelley M, Means-Powell J, Gore JC, Yankeelov TE. A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer. Physics in Medicine and Biology, 2011; 56:5753-69. PMCID: PMC3176673

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  1. Aerts, H. J. W. L. et al. Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC. Sci. Rep.(2016) 6, 33860 (link)

  2. Oliveira B, O'Halloran M, Conceicao R, Glavin M, Jones E. Development of Clinically-Informed 3D Tumor Models for Microwave Imaging Applications. IEEE Antennas and Wireless Propagation Letters 2016;15:520-3. doi: 10.1109/LAWP.2015.2456051

  3. Melouah A. Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique.  Computer Science and Its Applications: Springer; 2015. p. 119-28.
  4. Aerts HJ, Velazquez ER, Leijenaar RTH, Parmar C, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications, 2014. 5(4006). doi:10.1038/ncomms5006(link)
  5. Balagurunathan Y, Kumar V, Gu Y, Kim J, Wang H, Liu Y, Goldgof DB, Hall LO, Korn R, Zhao B. Test–Retest Reproducibility Analysis of Lung CT Image Features. Journal of digital imaging. 2014:1-19.

  6. Melouah, A. (2015). Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique. Computer Science and Its Applications, Springer: 119-128.

  7. Desseroit M-C, Visvikis D, Tixier F, Majdoub M, Perdrisot R, Guillevin R, Le Rest CC, Hatt M. Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III. European journal of nuclear medicine and molecular imaging. 2016:1-9. http://dx.doi.org/10.1007/s00259-016-3325-5

 

Info
titleThe following source material predate TCIA but These refer to the data in this CollectionRIDER Collections data, created before submission to TCIA
  1. Meyer CR, Armato SG III, Fenimore CP, McLennan G, Bidaut LM, Barboriak DP, Gavrielides MA, Jackson EF, McNitt-Gray MF, Kinahan PE, Petrick N, Zhao B. Quantitative imaging to assess tumor response to therapy: Common themes of measurement, truth data and error sources. Translational Oncology 2: 198–210, 2009. (link)
  2. McNitt-Gray MF, Bidaut LM, Armato SG III, Meyer CR, Gavrielides MA, Fenimore CP, McLennan G, Petrick N, Zhao B, Reeves AP, Beichel R, Kim H-J, Kinnard L. CT assessment of response to therapy: Tumor volume change measurement, truth data and error.Translational Oncology2009. 2:216–222. (link)
  3. Kinahan PE, Doot RK, Wanner-Roybal M, Bidaut LM, Armato SG III, Meyer CR, McLennan G.PET/CT assessment of response to therapy: Tumor change measurement, truth data and error.Translational Oncology 2:223–230, 2009. (link)
  4. Jackson EF, Barboriak DP, Bidaut LM, Meyer CR. Magnetic resonance assessment of response to therapy: tumor change measurement, truth data and error sources.Translational Oncology 2009 Dec;2(4):211-5. PubMed PMID: 19956380; PubMed Central PMCID: PMC2781079. (link)
  5. Armato SG 3rd, Meyer CR, Mcnitt-Gray MF, McLennan G, Reeves AP, Croft BY, Clarke LP;RIDER Research Group. The Reference Image Database to Evaluate Response to therapy in lung cancer (RIDER) project: a resource for the development of change-analysis software.Clin Pharmacol Ther. 2008 Oct;84(4):448-56. PubMed PMID: 18754000. (link)

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