Honors & Awards

  • Travel Fellowship Award, International Society for Molecular Biology (2011)
  • Honorarium for excellence in radiogenomics brain tumor research collaborations, Departments of Neurosurgery and Neuroradiology, Stanford University (2013-2014)

Education & Certifications

  • Master of Science, Stanford University, BIOM-MS (2013)
  • Bachelor of Applied Science, University of Waterloo, Computer Science/Bioinformatic (2009)

Stanford Advisors

Research & Scholarship

Current Research and Scholarly Interests

Riyana Basu, Tiffany T. Liu, Rajan Jain, Daniel L. Rubin. Reproducibility and Variability of Hemodynamic Measures in Dynamic Susceptibility Contrast T2* Perfusion Imaging of Brain Tumors Quantified by Two Different Software Packages, NordicICE and IB Neuro. ASNR 52nd Annual Meeting 2014. Oral Presentation (Scientific Paper). May 2014


All Publications

  • Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Science translational medicine Itakura, H., Achrol, A. S., Mitchell, L. A., Loya, J. J., Liu, T., Westbroek, E. M., Feroze, A. H., Rodriguez, S., Echegaray, S., Azad, T. D., Yeom, K. W., Napel, S., Rubin, D. L., Chang, S. D., Harsh, G. R., Gevaert, O. 2015; 7 (303): 303ra138-?


    Glioblastoma (GBM) is the most common and highly lethal primary malignant brain tumor in adults. There is a dire need for easily accessible, noninvasive biomarkers that can delineate underlying molecular activities and predict response to therapy. To this end, we sought to identify subtypes of GBM, differentiated solely by quantitative magnetic resonance (MR) imaging features, that could be used for better management of GBM patients. Quantitative image features capturing the shape, texture, and edge sharpness of each lesion were extracted from MR images of 121 single-institution patients with de novo, solitary, unilateral GBM. Three distinct phenotypic "clusters" emerged in the development cohort using consensus clustering with 10,000 iterations on these image features. These three clusters-pre-multifocal, spherical, and rim-enhancing, names reflecting their image features-were validated in an independent cohort consisting of 144 multi-institution patients with similar tumor characteristics from The Cancer Genome Atlas (TCGA). Each cluster mapped to a unique set of molecular signaling pathways using pathway activity estimates derived from the analysis of TCGA tumor copy number and gene expression data with the PARADIGM (Pathway Recognition Algorithm Using Data Integration on Genomic Models) algorithm. Distinct pathways, such as c-Kit and FOXA, were enriched in each cluster, indicating differential molecular activities as determined by the image features. Each cluster also demonstrated differential probabilities of survival, indicating prognostic importance. Our imaging method offers a noninvasive approach to stratify GBM patients and also provides unique sets of molecular signatures to inform targeted therapy and personalized treatment of GBM.

    View details for DOI 10.1126/scitranslmed.aaa7582

    View details for PubMedID 26333934

  • MRI Surrogates for Molecular Subgroups of Medulloblastoma AMERICAN JOURNAL OF NEURORADIOLOGY Perreault, S., Ramaswamy, V., Achrol, A. S., Chao, K., Liu, T. T., Shih, D., Remke, M., Schubert, S., Bouffet, E., Fisher, P. G., Partap, S., Vogel, H., Taylor, M. D., Cho, Y. J., Yeom, K. W. 2014; 35 (7): 1263-1269


    Recently identified molecular subgroups of medulloblastoma have shown potential for improved risk stratification. We hypothesized that distinct MR imaging features can predict these subgroups.All patients with a diagnosis of medulloblastoma at one institution, with both pretherapy MR imaging and surgical tissue, served as the discovery cohort (n = 47). MR imaging features were assessed by 3 blinded neuroradiologists. NanoString-based assay of tumor tissues was conducted to classify the tumors into the 4 established molecular subgroups (wingless, sonic hedgehog, group 3, and group 4). A second pediatric medulloblastoma cohort (n = 52) from an independent institution was used for validation of the MR imaging features predictive of the molecular subtypes.Logistic regression analysis within the discovery cohort revealed tumor location (P < .001) and enhancement pattern (P = .001) to be significant predictors of medulloblastoma subgroups. Stereospecific computational analyses confirmed that group 3 and 4 tumors predominated within the midline fourth ventricle (100%, P = .007), wingless tumors were localized to the cerebellar peduncle/cerebellopontine angle cistern with a positive predictive value of 100% (95% CI, 30%-100%), and sonic hedgehog tumors arose in the cerebellar hemispheres with a positive predictive value of 100% (95% CI, 59%-100%). Midline group 4 tumors presented with minimal/no enhancement with a positive predictive value of 91% (95% CI, 59%-98%). When we used the MR imaging feature-based regression model, 66% of medulloblastomas were correctly predicted in the discovery cohort, and 65%, in the validation cohort.Tumor location and enhancement pattern were predictive of molecular subgroups of pediatric medulloblastoma and may potentially serve as a surrogate for genomic testing.

    View details for DOI 10.3174/ajnr.A3990

    View details for Web of Science ID 000339138200005

  • Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling TRANSLATIONAL ONCOLOGY Chenevert, T. L., Malyarenko, D. I., Newitt, D., Li, X., Jayatilake, M., Tudorica, A., Fedorov, A., Kikinis, R., Liu, T. T., Muzi, M., Oborski, M., Laymon, C. M., Li, X., Thomas, Y., Jayashree, K., Mountz, J. M., Kinahan, P. E., Rubin, D. L., Fennessy, F., Huang, W., Hylton, N., Ross, B. D. 2014; 7 (1): 65-71


    To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers.A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create "variable signal," whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity.Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images.Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.

    View details for DOI 10.1593/tlo.13811

    View details for Web of Science ID 000342684300009

    View details for PubMedID 24772209

  • Comprehensivemolecular characterization of clear cell renal cell carcinoma NATURE Creighton, C. J., Morgan, M., Gunaratne, P. H., Wheeler, D. A., Gibbs, R. A., Robertson, A. G., Chu, A., Beroukhim, R., Cibulskis, K., Signoretti, S., Vandin, F., Wu, H., Raphael, B. J., Verhaak, R. G., Tamboli, P., Torres-Garcia, W., Akbani, R., Weinstein, J. N., Reuter, V., Hsieh, J. J., Brannon, A. R., Hakimi, A. A., Jacobsen, A., Ciriello, G., Reva, B., Ricketts, C. J., Linehan, W. M., Stuart, J. M., Rathmell, W. K., Shen, H., Laird, P. W., Muzny, D., Davis, C., Morgan, M., Xi, L., Chang, K., Kakkar, N., Trevino, L. R., Benton, S., Reid, J. G., Morton, D., Doddapaneni, H., Han, Y., Lewis, L., Dinh, H., Kovar, C., Zhu, Y., Santibanez, J., Wang, M., Hale, W., Kalra, D., Creighton, C. J., Wheeler, D. A., Gibbs, R. A., Getz, G., Cibulskis, K., Lawrence, M. S., Sougnez, C., Carter, S. L., Sivachenko, A., Lichtenstein, L., Stewart, C., Voet, D., Fisher, S., Gabriel, S. B., Lander, E., Beroukhim, R., Schumacher, S. E., Tabak, B., Saksena, G., Onofrio, R. C., Carter, S. L., Cherniack, A. D., Gentry, J., Ardlie, K., Sougnez, C., Getz, G., Gabriel, S. B., Meyerson, M., Robertson, A. G., Chu, A., Chun, H. E., Mungall, A. J., Sipahimalani, P., Stoll, D., Ally, A., Balasundaram, M., Butterfield, Y. S., Carlsen, R., Carter, C., Chuah, E., Coope, R. J., Dhalla, N., Gorski, S., Guin, R., Hirst, C., Hirst, M., Holt, R. A., Lebovitz, C., Lee, D., Li, H. I., Mayo, M., Moore, R. A., Pleasance, E., Plettner, P., Schein, J. E., Shafiei, A., Slobodan, J. R., Tam, A., Thiessen, N., Varhol, R. J., Wye, N., Zhao, Y., Birol, I., Jones, S. J., Marra, M. A., Auman, J. T., Tan, D., Jones, C. D., Hoadley, K. A., Mieczkowski, P. A., Mose, L. E., Jefferys, S. R., Topal, M., Liquori, C., Turman, Y. J., Shi, Y., Waring, S., Buda, E., Walsh, J., Wu, J., Bodenheimer, T., Hoyle, A. P., Simons, J. V., Soloway, M., Balu, S., Parker, J. S., Hayes, D. N., Perou, C. M., Kucherlapati, R., Park, P., Shen, H., Triche, T., Weisenberger, D. J., Lai, P. H., Bootwalla, M. S., Maglinte, D. T., Mahurkar, S., Berman, B. P., Van den Berg, D. J., Cope, L., Baylin, S. B., Laird, P. W., Creighton, C. J., Wheeler, D. A., Getz, G., Noble, M. S., DiCara, D., Zhang, H., Cho, J., Heiman, D. I., Gehlenborg, N., Voet, D., Mallard, W., Lin, P., Frazer, S., Stojanov, P., Liu, Y., Zhou, L., Kim, J., Lawrence, M. S., Chin, L., Vandin, F., Wu, H., Raphael, B. J., Benz, C., Yau, C., Reynolds, S. M., Shmulevich, I., Verhaak, R. G., Torres-Garcia, W., Vegesna, R., Kim, H., Zhang, W., Cogdell, D., Jonasch, E., Ding, Z., Lu, Y., Akbani, R., Zhang, N., Unruh, A. K., Casasent, T. D., Wakefield, C., Tsavachidou, D., Chin, L., Mills, G. B., Weinstein, J. N., Jacobsen, A., Brannon, A. R., Ciriello, G., Schultz, N., Hakimi, A. A., Reva, B., Antipin, Y., Gao, J., Cerami, E., Gross, B., Aksoy, B. A., Sinha, R., Weinhold, N., Sumer, S. O., Taylor, B. S., Shen, R., Ostrovnaya, I., Hsieh, J. J., Berger, M. F., Ladanyi, M., Sander, C., Fei, S. S., Stout, A., Spellman, P. T., Rubin, D. L., Liu, T. T., Stuart, J. M., Sam Ng, S., Paull, E. O., Carlin, D., Goldstein, T., Waltman, P., Ellrott, K., Zhu, J., Haussler, D., Gunaratne, P. H., Xiao, W., Shelton, C., Gardner, J., Penny, R., Sherman, M., Mallery, D., Morris, S., Paulauskis, J., Burnett, K., Shelton, T., Signoretti, S., Kaelin, W. G., Choueiri, T., Atkins, M. B., Penny, R., Burnett, K., Mallery, D., Curley, E., Tickoo, S., Reuter, V., Rathmell, W. K., Thorne, L., Boice, L., Huang, M., Fisher, J. C., Linehan, W. M., Vocke, C. D., Peterson, J., Worrell, R., Merino, M. J., Schmidt, L. S., Tamboli, P., Czerniak, B. A., Aldape, K. D., Wood, C. G., Boyd, J., Weaver, J., Iacocca, M. V., Petrelli, N., Witkin, G., Brown, J., Czerwinski, C., Huelsenbeck-Dill, L., Rabeno, B., Myers, J., Morrison, C., Bergsten, J., Eckman, J., Harr, J., Smith, C., Tucker, K., Zach, L. A., Bshara, W., Gaudioso, C., Morrison, C., Dhir, R., Maranchie, J., Nelson, J., Parwani, A., Potapova, O., Fedosenko, K., Cheville, J. C., Thompson, R. H., Signoretti, S., Kaelin, W. G., Atkins, M. B., Tickoo, S., Reuter, V., Linehan, W. M., Vocke, C. D., Peterson, J., Merino, M. J., Schmidt, L. S., Tamboli, P., Mosquera, J. M., Rubin, M. A., Blute, M. L., Rathmell, W. K., Pihl, T., Jensen, M., Sfeir, R., Kahn, A., Chu, A., Kothiyal, P., Snyder, E., Pontius, J., Ayala, B., Backus, M., Walton, J., Baboud, J., Berton, D., Nicholls, M., Srinivasan, D., Raman, R., Girshik, S., Kigonya, P., Alonso, S., Sanbhadti, R., Barletta, S., Pot, D., Sheth, M., Demchok, J. A., Davidsen, T., Wang, Z., Yang, L., Tarnuzzer, R. W., Zhang, J., Eley, G., Ferguson, M. L., Shaw, K. R., Guyer, M. S., Ozenberger, B. A., Sofia, H. J. 2013; 499 (7456): 43-?


    Genetic changes underlying clear cell renal cell carcinoma (ccRCC) include alterations in genes controlling cellular oxygen sensing (for example, VHL) and the maintenance of chromatin states (for example, PBRM1). We surveyed more than 400 tumours using different genomic platforms and identified 19 significantly mutated genes. The PI(3)K/AKT pathway was recurrently mutated, suggesting this pathway as a potential therapeutic target. Widespread DNA hypomethylation was associated with mutation of the H3K36 methyltransferase SETD2, and integrative analysis suggested that mutations involving the SWI/SNF chromatin remodelling complex (PBRM1, ARID1A, SMARCA4) could have far-reaching effects on other pathways. Aggressive cancers demonstrated evidence of a metabolic shift, involving downregulation of genes involved in the TCA cycle, decreased AMPK and PTEN protein levels, upregulation of the pentose phosphate pathway and the glutamine transporter genes, increased acetyl-CoA carboxylase protein, and altered promoter methylation of miR-21 (also known as MIR21) and GRB10. Remodelling cellular metabolism thus constitutes a recurrent pattern in ccRCC that correlates with tumour stage and severity and offers new views on the opportunities for disease treatment.

    View details for DOI 10.1038/nature12222

    View details for Web of Science ID 000321285600029

    View details for PubMedID 23792563

  • Erratum to: Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers. Journal of digital imaging Buckler, A. J., Liu, T. T., Savig, E., Suzek, B. E., Rubin, D. L., Paik, D. 2013; 26 (4): 642

    View details for PubMedID 23681450

  • Automatic annotation of radiological observations in liver CT images. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Gimenez, F., Xu, J., Liu, Y., Liu, T., Beaulieu, C., Rubin, D., Napel, S. 2012; 2012: 257-263


    We aim to predict radiological observations using computationally-derived imaging features extracted from computed tomography (CT) images. We created a dataset of 79 CT images containing liver lesions identified and annotated by a radiologist using a controlled vocabulary of 76 semantic terms. Computationally-derived features were extracted describing intensity, texture, shape, and edge sharpness. Traditional logistic regression was compared to L(1)-regularized logistic regression (LASSO) in order to predict the radiological observations using computational features. The approach was evaluated by leave one out cross-validation. Informative radiological observations such as lesion enhancement, hypervascular attenuation, and homogeneous retention were predicted well by computational features. By exploiting relationships between computational and semantic features, this approach could lead to more accurate and efficient radiology reporting.

    View details for PubMedID 23304295

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