Visual Analytics for the Exploration of Tumor Tissue Characterization
Tumors are heterogeneous tissues consisting of multiple regions with distinct characteristics. Characterization of these intra‐tumor regions can improve patient diagnosis and enable a better targeted treatment. Ideally, tissue characterization could be performed non‐invasively, using medical imaging...
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| Vydáno v: | Computer graphics forum Ročník 34; číslo 3; s. 11 - 20 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Oxford
Blackwell Publishing Ltd
01.06.2015
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Tumors are heterogeneous tissues consisting of multiple regions with distinct characteristics. Characterization of these intra‐tumor regions can improve patient diagnosis and enable a better targeted treatment. Ideally, tissue characterization could be performed non‐invasively, using medical imaging data, to derive per voxel a number of features, indicative of tissue properties. However, the high dimensionality and complexity of this imaging‐derived feature space is prohibiting for easy exploration and analysis ‐ especially when clinical researchers require to associate observations from the feature space to other reference data, e.g., features derived from histopathological data. Currently, the exploratory approach used in clinical research consists of juxtaposing these data, visually comparing them and mentally reconstructing their relationships. This is a time consuming and tedious process, from which it is difficult to obtain the required insight. We propose a visual tool for: (1) easy exploration and visual analysis of the feature space of imaging‐derived tissue characteristics and (2) knowledge discovery and hypothesis generation and confirmation, with respect to reference data used in clinical research. We employ, as central view, a 2D embedding of the imaging‐derived features. Multiple linked interactive views provide functionality for the exploration and analysis of the local structure of the feature space, enabling linking to patient anatomy and clinical reference data. We performed an initial evaluation with ten clinical researchers. All participants agreed that, unlike current practice, the proposed visual tool enables them to identify, explore and analyze heterogeneous intra‐tumor regions and particularly, to generate and confirm hypotheses, with respect to clinical reference data. |
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| AbstractList | Tumors are heterogeneous tissues consisting of multiple regions with distinct characteristics. Characterization of these intra‐tumor regions can improve patient diagnosis and enable a better targeted treatment. Ideally, tissue characterization could be performed non‐invasively, using medical imaging data, to derive per voxel a number of features, indicative of tissue properties. However, the high dimensionality and complexity of this imaging‐derived feature space is prohibiting for easy exploration and analysis ‐ especially when clinical researchers require to associate observations from the feature space to other reference data, e.g., features derived from histopathological data. Currently, the exploratory approach used in clinical research consists of juxtaposing these data, visually comparing them and mentally reconstructing their relationships. This is a time consuming and tedious process, from which it is difficult to obtain the required insight. We propose a visual tool for: (1) easy exploration and visual analysis of the feature space of imaging‐derived tissue characteristics and (2) knowledge discovery and hypothesis generation and confirmation, with respect to reference data used in clinical research. We employ, as central view, a 2D embedding of the imaging‐derived features. Multiple linked interactive views provide functionality for the exploration and analysis of the local structure of the feature space, enabling linking to patient anatomy and clinical reference data. We performed an initial evaluation with ten clinical researchers. All participants agreed that, unlike current practice, the proposed visual tool enables them to identify, explore and analyze heterogeneous intra‐tumor regions and particularly, to generate and confirm hypotheses, with respect to clinical reference data. |
| Author | Dinh, C.V. Kallehauge, J.F. Raidou, R.G. Breeuwer, M. Vilanova, A. Ghobadi, G. van der Heide, U.A. |
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| Cites_doi | 10.1109/MCG.2009.45 10.1016/j.ijrobp.2011.07.021 10.1007/978-0-387-45528-0 10.1109/VAST.2010.5652392 10.1145/1268517.1268563 10.1109/MC.2002.1016905 10.2307/2289444 10.1097/01.pas.0000173646.99337.b1 10.1016/j.mric.2008.07.002 10.1111/j.1467-8659.2011.01920.x 10.1137/0906011 10.1016/j.cag.2010.05.016 10.5220/0004719901690176 10.1016/S0167-8140(15)30519-3 10.1109/TVCG.2007.70569 10.1109/VISUAL.1994.346302 10.1109/TVCG.2011.279 10.1109/TVCG.2008.95 10.1109/VAST.2010.5652443 10.1002/nbm.2940 10.1007/BF01898350 |
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| Copyright | 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2015 The Eurographics Association and John Wiley & Sons Ltd. |
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Visualization and Computer Graphics, IEEE Transactions on 15, 2 (2009), 205-220. 3 – reference: Lam H., Bertini E., Isenberg P., Plaisant C., Carpendale S.: Empirical studies in information visualization: Seven scenarios. IEEE Transactions on Visualization and Computer Graphics 18, 9 (2012), 1520-1536. 6 – reference: Kallehauge J., Haack S., Lindegaard J., Fokdal L., Mohamed S., Tanderup K., Pedersen E., Nielsen T.: Tracer kinetic model selection for dynamic contrast-enhanced MRI of locally advanced cervical cancer. Radiotherapy and Oncology 111 (2014), 174. 2, 8 – reference: Van der Maaten L., Hinton G.: Visualizing high-dimensional data using t-SNE. 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| SubjectTerms | Analysis Categories and Subject Descriptors (according to ACM CCS) Computer graphics Consumption Data mining Exploration I.3.8 [Computer Graphics]: Applications-Applications J.3 [Computer Applications]: Life and Medical Sciences-Life and Medical Sciences Medical diagnosis Medical imaging Medical services Patients Studies Tumors Visual Visualization |
| Title | Visual Analytics for the Exploration of Tumor Tissue Characterization |
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