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  1. Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroyer KR, Zhao T, Batiste R, Van Arnam J, Cancer Genome Atlas Research N, Shmulevich I, Rao AUK, Lazar AJ, Sharma A, Thorsson V. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep.

    2018;23(1):181-93 e7. DOI: http://doi.org/10.1016/j.celrep.2018.03.086
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Algorithm Development

  1. Yassine, A.-A., Lilge, L., & Betz, V. (2018). Optimizing interstitial photodynamic therapy with custom cylindrical diffusers. Journal of Biophotonics. DOI: 10.1002/jbio.201800153
  2. Men, K., Geng, H., Cheng, C., Zhong, H., Huang, M., Fan, Y., Plastaras, J. P., Lin, A., Xiao, Y. (2018). More accurate and efficient segmentation of organs-at-risk in radiotherapy with Convolutional Neural Networks Cascades. Medical Physics. DOI: 10.1002/mp.13296 

  3. Edalati-rad, A., & Mosleh, M. (2018). Improving brain tumor diagnosis using MRI segmentation based on collaboration of beta mixture model and learning automata. Arabian Journal for Science and Engineering, 1-13. DOI:10.1007/s13369-018-3320-1

  4. Taghanaki, S. A., Duggan, M., Ma, H., Hou, X., Celler, A., Benard, F., Hamarneh, G. (2017). Segmentation-free direct tumor volume and metabolic activity estimation from PET scans. Computerized Medical Imaging and Graphics, 63, 53-56. DOI: 10.1016/j.compmedimag.2017.12.004

  5. Y Ren, J Ma, J Xiong, Y Chen, L Lu, J Zhao (2018) Improved False Positive Reduction by Novel Morphological Features for Computer-Aided Polyp Detection in CT Colonography. IEEE Journal of Biomedical and Health Informatics. DOI:10.1109/JBHI.2018.2808199

  6. Babu, J. S., Mathew, S., & Simon, R. (2017). Biomedical image retrieval using LBWPInternational Research Journal of Engineering and Technology (IRJET), 4(9), 839-843. https://www.irjet.net/archives/V4/i9/IRJET-V4I9147.pdf
  7. Hostetter, J. M., Morrison, J. J., Morris, M., Jeudy, J., Wang, K. C., & Siegel, E. (2017). Personalizing lung cancer risk prediction and imaging follow-up recommendations using the National Lung Screening Trial dataset. Journal of the American Medical Informatics Association, 24(6), 1046-1051. DOI:10.1093/jamia/ocx012

  8. Mason J, Perelli A, Nailon W, Davies M. (2017) Can Planning Images Reduce Scatter in Follow-Up Cone-Beam CT? arXiv 1703.07179
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  11. Yang H, Liu F, Wang Z, Tang H, Sun S, Sun S. Research on the Content-Based Classification of Medical Image. Journal of Medical Imaging and Health Informatics. 2017;7(1):129-36. (link)

  12. Rezaie AA, Habiboghli A. Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation. International Journal of Interactive Multimedia and Artificial Inteligence. 2017;4(Special Issue on 3D Medicine and Artificial Intelligence):15-9. (link)

  13. Chen H, Zhang Y, Zhang W, Liao P, Li K, Zhou J, Wang G. Low-dose CT via convolutional neural network. Biomedical Optics Express. 2017;8(2):679-94.(link)

  14. Vallières M, Freeman C, Skamene S, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Physics in medicine and biology. 2015;60(14):5471.
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  34. Sivakumar S, and Chandrasekar C. A Study on Image Denoising for Lung CT Scan Images.International Journal of Emerging Technologies in Computational and Applied Sciences, 2014. 7(1):86-91 (link)
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  36. Harmon S, Wendelberger B, and Jeraj R. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis. Medical Physics, 2014. 41(6): p. 178-178. DOI:10.1118/1.4888150 (link)
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  41. Breseman K, Lee C, Bloch BN, and Jaffe C. Constructing 3D-Printable CAD Models of Prostates from MR Images. Bioengineering Conference (NEBEC), 39th Annual Northeast , IEEE, 27-28. 5-7 April 2013. DOI:10.1109/NEBEC.2013.8
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  62. Chaddad, A. and C. Tanougast "High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features." Advances in Bioinformatics 2015. DOI: 10.1155/2015/728164 
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Info
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  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., Ductal 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).

Collection: NLST

Please see List of NLST Publications at NIH to browse publications from this Data Collection.

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  1. .MRI.Ultrasound.Summary.pdf).
  2. Jansen S., Investigating genetic events in the progression of ductal carcinoma in situ (Mouse.Mammary.Genetics.DCIS.pdf).

Collection: NLST

Please see List of NLST Publications at NIH to browse publications from this Data Collection.

Collection: NSCLC-Radiomics

  1. L Yang, J Yang, X Zhou, L Huang, W Zhao, T Wang, J Zhuang, J Tian. (2018) Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients. European Radiology, 2018 DOI:  10.1007/s00330-018-5770-y
  2. Lee, J., Cui, Y., Sun, X., Li, B., Wu, J., Li, D., Gensheimer, M. F., Loo Jr., B. W., Diehn, M., Li, R. (2017). Prognostic value and molecular correlates of a CT image-based quantitative pleural contact index in early stage NSCLCEuropean Radiology, 1-11. DOI:  10.1007s00330-017-4996-4 
  3. Soufi M, Arimura H, Nakamoto T, Hirose T-A, Ohga S, Umezu Y, Honda H, Sasaki T. (2018). Exploration of temporal stability and prognostic power of radiomic features based on electronic portal imaging device images. Physica Medica, 46:32-44. DOI: 10.1016/j.ejmp.2017.11.037

  4. Patil R, Mahadevaiah G, Dekker A. An Approach Toward Automatic Classification of Tumor Histopathology of Non–Small Cell Lung Cancer Based on Radiomic Features. Tomography: a journal for imaging research. 2016;2(4):374-7. (link)

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  1. Park SY and Sargent D. Tumor propagation model using generalized hidden Markov model. Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331G February 24, 2017); 10.1117/12.2254583
  2. Sargent D, Park S-Y. Semi-automatic 3D lung nodule segmentation in CT using dynamic programming. Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332R (February 24, 2017) DOI: 10.1117/12.2254575
  3. Nishio M, Nagashima C. Computer-aided Diagnosis for Lung Cancer: Usefulness of Nodule Heterogeneity. Academic Radiology. 2017;24(3):328-36. (link)
  4. SPIE-AAPM-NCI PROSTATEx Challenges

Collection: TCGA-BRCA

  1. Lehrer, M., Bhadra, A., Aithala, S., Ravikumar, V., Zheng, Y., Dogan, B., Bonaccio, E., Burnside, E. S., Morris, E., Sutton, E., Whitman, G. J., Net, J., Brandt, K., Ganott, M., Zuley, M., Rao, A., & TCGA Breast Phenotype Research Group. (2018). High-dimensional regression analysis links magnetic resonance imaging features and protein expression and signaling pathway alterations in breast invasive carcinoma. Oncoscience, 5(1-2), 39-48. (link)

  2. Al-Dabagh MZ, AL-Mukhtar FH. Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine. IJAERS. 2017;4(3):258-63. DOI: 10.22161/ijaers.4.3.41

  3. Angela Giardino, Supriya Gupta, Emmi Olson, Karla Sepulveda, Leon Lenchik, Jana Ivanidze, Rebecca Rakow-Penner, Midhir J. Patel, Rathan M. Subramaniam, Dhakshinamoorthy Ganeshan. Role of Imaging in the Era of Precision Medicine. Academic Radiology, Available online 25 January 2017 DOI: 10.1016/j.acra.2016.11.021
  4. Albiol, Alberto; Corbi, Alberto; Albiol, Francisco. Automatic intensity windowing of mammographic images based on a perceptual metric. Medical Physics, 2473-4209.10.1002/mp.12144 
  5. Wu, J; Sun, X; Wang, J; Cui, Y;  Kato, F; Shirato, H; Ikeda, DM.; Li, R. Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation. Journal of Magnetic Resonance Imaging, 2586 DOI: 10.1002/jmri.25661
  6. Wu J, Cui Y, Sun X, Cao G, Li B, Ikeda DM, Kurian AW, Li R. Unsupervised clustering of quantitative image phenotypes reveals breast cancer subtypes with distinct prognoses and molecular pathways. Clinical Cancer Research. 2017:clincanres. 2415.016. (link)

  7. Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging. Radiology, 2014. DOI: 10.1148/radiol.14132641 (link)
  8. Lavasani, S. N., A. F. Kazerooni, et al. (2015). Discrimination of Benign and Malignant Suspicious BreastTumors Based on Semi-Quantitative DCE-MRI ParametersEmploying Support Vector Machine. Frontiers in Biomedical Technologies 2(2): 397-403.

  9. Anand, S., V. Vinod, et al. Application of Fuzzy c-means and Neural networks to categorize tumor affected breast MR Images. International Journal of Applied Engineering Research 10(64): 2015.

  10. Guo, W., H. Li, et al. (2015). Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. Journal of Medical Imaging 2(4): 041007-041007.

  11. Kim, G. R., Ku, Y. J., Cho, S. G., Kim, S. J., & Min, B. S. (2017). Associations between gene expression profiles of invasive breast cancer and breast imaging reporting and data system MRI lexicon. Annals of Surgical Treatment and Research, 93(1), 18-26. DOI: 10.4174/astr.2017.93.1.18

     

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  1. Halani, S. H., Yousefi, S.; Vega, J. V.; Rossi, M. R.; Zhao, Z.; Amrollahi, F.; Holder, C. A.; Baxter-Stoltzfus, A.; Eschbacher, J.; Griffith, B.; Olson, J. J.; Jiang, T.; Yates, J. R.; Eberhart, C. G.; Poisson, L. M.; Cooper, L. A. D.; Brat, D. J. (2018). Multi-faceted computational assessment of risk and progression in oligodendroglioma implicates NOTCH and PI3K pathways. Precision Oncology. DOI: 10.1038/s41698-018-0067-9 

  2. Liu, Z., Zhang, T., Jiang, H., Xu, W., & Zhang, J. (2018). Conventional MR-based preoperative nomograms for prediction of IDH/1p19q subtype in low-grade glioma. Academic Radiology. DOI: 10.1016/j.acra.2018.09.022 

Collection: TCGA-LUAD

  1. Dara S, Tumma P, Eluri N, Kancharla G. Feature Extraction In Medical Images by Using Deep Learning Approach. International Journal of Pure and Applied Mathematics. 2018;120(6):305-12.

  2. Pathak, Y., Arya, K. V., & Tiwari, S. (2018). An efficient low-dose CT reconstruction technique using partial derivatives based guided image filter. Multimedia Tools and Applications, 1-20. DOI: 10.1007/s11042-018-6840-5 

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