Browsing by Author "Blocker, Stephanie J"

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    An Online Repository for Pre-Clinical Imaging Protocols (PIPs).
    (Tomography (Ann Arbor, Mich.), 2023-03) Gammon, Seth T; Cohen, Allison S; Lehnert, Adrienne L; Sullivan, Daniel C; Malyarenko, Dariya; Manning, Henry Charles; Hormuth, David A; Daldrup-Link, Heike E; An, Hongyu; Quirk, James D; Shoghi, Kooresh; Pagel, Mark David; Kinahan, Paul E; Miyaoka, Robert S; Houghton, A McGarry; Lewis, Michael T; Larson, Peder; Sriram, Renuka; Blocker, Stephanie J; Pickup, Stephen; Badea, Alexandra; Badea, Cristian T; Yankeelov, Thomas E; Chenevert, Thomas L
    Providing method descriptions that are more detailed than currently available in typical peer reviewed journals has been identified as an actionable area for improvement. In the biochemical and cell biology space, this need has been met through the creation of new journals focused on detailed protocols and materials sourcing. However, this format is not well suited for capturing instrument validation, detailed imaging protocols, and extensive statistical analysis. Furthermore, the need for additional information must be counterbalanced by the additional time burden placed upon researchers who may be already overtasked. To address these competing issues, this white paper describes protocol templates for positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance imaging (MRI) that can be leveraged by the broad community of quantitative imaging experts to write and self-publish protocols in protocols.io. Similar to the Structured Transparent Accessible Reproducible (STAR) or Journal of Visualized Experiments (JoVE) articles, authors are encouraged to publish peer reviewed papers and then to submit more detailed experimental protocols using this template to the online resource. Such protocols should be easy to use, readily accessible, readily searchable, considered open access, enable community feedback, editable, and citable by the author.
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    Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine.
    (Tomography (Ann Arbor, Mich.), 2020-09) Shoghi, Kooresh I; Badea, Cristian T; Blocker, Stephanie J; Chenevert, Thomas L; Laforest, Richard; Lewis, Michael T; Luker, Gary D; Manning, H Charles; Marcus, Daniel S; Mowery, Yvonne M; Pickup, Stephen; Richmond, Ann; Ross, Brian D; Vilgelm, Anna E; Yankeelov, Thomas E; Zhou, Rong
    The National Institutes of Health's (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine.
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    Ex Vivo MR Histology and Cytometric Feature Mapping Connect Three-dimensional in Vivo MR Images to Two-dimensional Histopathologic Images of Murine Sarcomas.
    (Radiology. Imaging cancer, 2021-05) Blocker, Stephanie J; Cook, James; Mowery, Yvonne M; Everitt, Jeffrey I; Qi, Yi; Hornburg, Kathryn J; Cofer, Gary P; Zapata, Fernando; Bassil, Alex M; Badea, Cristian T; Kirsch, David G; Johnson, G Allan
    Purpose To establish a platform for quantitative tissue-based interpretation of cytoarchitecture features from tumor MRI measurements. Materials and Methods In a pilot preclinical study, multicontrast in vivo MRI of murine soft-tissue sarcomas in 10 mice, followed by ex vivo MRI of fixed tissues (termed MR histology), was performed. Paraffin-embedded limb cross-sections were stained with hematoxylin-eosin, digitized, and registered with MRI. Registration was assessed by using binarized tumor maps and Dice similarity coefficients (DSCs). Quantitative cytometric feature maps from histologic slides were derived by using nuclear segmentation and compared with registered MRI, including apparent diffusion coefficients and transverse relaxation times as affected by magnetic field heterogeneity (T2* maps). Cytometric features were compared with each MR image individually by using simple linear regression analysis to identify the features of interest, and the goodness of fit was assessed on the basis of R2 values. Results Registration of MR images to histopathologic slide images resulted in mean DSCs of 0.912 for ex vivo MR histology and 0.881 for in vivo MRI. Triplicate repeats showed high registration repeatability (mean DSC, >0.9). Whole-slide nuclear segmentations were automated to detect nuclei on histopathologic slides (DSC = 0.8), and feature maps were generated for correlative analysis with MR images. Notable trends were observed between cell density and in vivo apparent diffusion coefficients (best line fit: R2 = 0.96, P < .001). Multiple cytoarchitectural features exhibited linear relationships with in vivo T2* maps, including nuclear circularity (best line fit: R2 = 0.99, P < .001) and variance in nuclear circularity (best line fit: R2 = 0.98, P < .001). Conclusion An infrastructure for registering and quantitatively comparing in vivo tumor MRI with traditional histologic analysis was successfully implemented in a preclinical pilot study of soft-tissue sarcomas. Keywords: MRI, Pathology, Animal Studies, Tissue Characterization Supplemental material is available for this article. © RSNA, 2021.
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    Whole-slide cytometric feature mapping for distinguishing tumor genomic subtypes in HNSCC whole slide images.
    (The American journal of pathology, 2022-11) Blocker, Stephanie J; Morrison, Samantha; Everitt, Jeffrey I; Cook, James; Luo, Sheng; Watts, Tammara L; Mowery, Yvonne M
    Head and neck squamous cell carcinoma (HNSCC) is a heterogenous disease where, in advanced stages, clinical and pathological stages do not correlate with outcome. Molecular and genomic biomarkers for HNSCC classification have shown promise for prognostic and therapeutic applications. In this study, we utilize automated image analysis techniques in whole slide images of HNSCC tumors to identify relationships between cytometric features and genomic phenotypes. Hematoxylin and eosin-stained slides of HNSCC tumors (N=49) were obtained from the Cancer Imaging Archive (TCIA), along with accompanying clinical, pathological, genomic, and proteomic reports. Automated nuclear detection was performed across the entirety of slides, and cytometric feature maps were generated. Forty-one cytometric features were evaluated for associations with tumor grade, tumor stage, tumor subsite, and integrated genomic subtype (IGS). Thirty-two features demonstrated significant association with IGS when corrected for multiple comparisons. In particular, the basal subtype was visually distinguishable from the chromosomal instability and immune subtypes based on cytometric feature measurements. No features were significantly associated with tumor grade, stage, or subsite. This study provides preliminary evidence that features derived from tissue pathology slides could provide insights into genomic phenotypes of HNSCC.