Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots

We present the design and evaluation of an integrated problem solving environment for cancer therapy analysis. The environment intertwines a statistical martingale model and a K Nearest Neighbor approach with visual encodings, including novel interactive nomograms, in order to compute and explain a patient's probability of survival as a function of similar patient results. A coordinated views paradigm enables exploration of the multivariate, heterogeneous and few-valued data from a large head and neck cancer repository. A visual scaffolding approach further enables users to build from familiar representations to unfamiliar ones. Evaluation with domain experts show how this visualization approach and set of streamlined workflows enable the systematic and precise analysis of a patient prognosis in the context of cohorts of similar patients. We describe the design lessons learned from this successful, multi-site remote collaboration.

[1]  Gerald McMahon,et al.  SU11248 maintenance therapy prevents tumor regrowth after fractionated irradiation of murine tumor models. , 2003, Cancer research.

[2]  Frank van Ham,et al.  “Search, Show Context, Expand on Demand”: Supporting Large Graph Exploration with Degree-of-Interest , 2009, IEEE Transactions on Visualization and Computer Graphics.

[3]  Laurie A. Williams,et al.  Pair Programming Illuminated , 2002 .

[4]  Kai Lawonn,et al.  Interactive Visual Analysis of Image-Centric Cohort Study Data , 2014, IEEE Transactions on Visualization and Computer Graphics.

[5]  Jacques Bernier,et al.  Molecular therapy in head and neck oncology , 2009, Nature Reviews Clinical Oncology.

[6]  G. Elisabeta Marai,et al.  GRACE: A Visual Comparison Framework for Integrated Spatial and Non-Spatial Geriatric Data , 2013, IEEE Transactions on Visualization and Computer Graphics.

[7]  S. Horvath,et al.  Global histone modification patterns predict risk of prostate cancer recurrence , 2005, Nature.

[8]  Jeffrey Heer,et al.  Scented Widgets: Improving Navigation Cues with Embedded Visualizations , 2007, IEEE Transactions on Visualization and Computer Graphics.

[9]  John D. Kalbfleisch,et al.  The Statistical Analysis of Failure Data , 1986, IEEE Transactions on Reliability.

[10]  John D Minna,et al.  New molecularly targeted therapies for lung cancer. , 2007, The Journal of clinical investigation.

[11]  Daniel L. Schwartz,et al.  Parallel prototyping leads to better design results, more divergence, and increased self-efficacy , 2010, TCHI.

[12]  Matthew O. Ward,et al.  A Taxonomy of Glyph Placement Strategies for Multidimensional Data Visualization , 2002, Inf. Vis..

[13]  George Fountzilas,et al.  Clinical and molecular prognostic factors in operable laryngeal cancer , 2005, European Archives of Oto-Rhino-Laryngology and Head & Neck.

[14]  Joseph Berkson,et al.  Survival Curve for Cancer Patients Following Treatment , 1952 .

[15]  Laurence L. George,et al.  The Statistical Analysis of Failure Time Data , 2003, Technometrics.

[16]  Tamara Munzner,et al.  Design Study Methodology: Reflections from the Trenches and the Stacks , 2012, IEEE Transactions on Visualization and Computer Graphics.

[17]  Zhiyuan Zhang,et al.  Iterative cohort analysis and exploration , 2015, Inf. Vis..

[18]  Jacob G. Scott,et al.  Impact of Pretreatment Volumetric Tumor Growth Velocity on Oncologic Outcomes in Oropharyngeal Squamous Cell Cancer , 2016 .

[19]  Martin Theus,et al.  High-dimensional Data Visualization , 2008 .

[20]  Kujtim Latifi,et al.  Proliferation Saturation Index Predicts Oropharyngeal Squamous Cell Cancer Gross Tumor Volume Reduction to Prospectively Identify Patients for Adaptive Radiation Therapy , 2016 .

[21]  J. Reiber,et al.  Integrated Visual Analysis for Heterogeneous Datasets in Cohort Studies , 2010 .

[22]  D. Hallahan,et al.  SU11248 (sunitinib) sensitizes pancreatic cancer to the cytotoxic effects of ionizing radiation. , 2007, International journal of radiation oncology, biology, physics.

[23]  E. Naumova,et al.  Visual Analytics for Epidemiologists: Understanding the Interactions Between Age, Time, and Disease with Multi-Panel Graphs , 2011, PloS one.

[24]  Jürgen Bernard,et al.  A Visual-interactive System for Prostate Cancer Stratifications , 2014 .

[25]  Radhe Mohan,et al.  Multiple regions-of-interest analysis of setup uncertainties for head-and-neck cancer radiotherapy. , 2006, International journal of radiation oncology, biology, physics.

[26]  H. Hofmann.  Exploring categorical data: interactive mosaic plots , 2000 .

[27]  G. Elisabeta Marai.  Visual Scaffolding in Integrated Spatial and Nonspatial Analysis , 2015, EuroVA@EuroVis.

[28]  Tamara Munzner,et al.  Visualization Analysis and Design , 2014, A.K. Peters visualization series.

[29]  Valerio Pascucci,et al.  Visualizing High-Dimensional Data: Advances in the Past Decade , 2017, IEEE Transactions on Visualization and Computer Graphics.

[30]  Tamara Munzner,et al.  A Nested Model for Visualization Design and Validation , 2009, IEEE Transactions on Visualization and Computer Graphics.

[31]  G. Elisabeta Marai,et al.  Conditional Survival Analysis of Patients With Locally Advanced Laryngeal Cancer: Construction of a Dynamic Risk Model and Clinical Nomogram , 2017, Scientific Reports.

[32]  Kwan-Liu Ma,et al.  A richly interactive exploratory data analysis and visualization tool using electronic medical records , 2015, BMC Medical Informatics and Decision Making.

[33]  Martin Theus,et al.  Interactive Data Visualization using Mondrian , 2002 .

[34]  Arvid Lundervold,et al.  Hypothesis Generation by Interactive Visual Exploration of Heterogeneous Medical Data , 2013, CHI-KDD.

[35]  Colin Potts,et al.  Design of Everyday Things , 1988 .

[36]  Stuart Wray.  How Pair Programming Really Works , 2010, IEEE Software.

[37]  Krist Wongsuphasawat,et al.  Outflow : Visualizing Patient Flow by Symptoms and Outcome , 2011 .

[38]  Kunihiko Tateoka,et al.  The Reproducibility of Patient Setup for Head and Neck Cancers Treated with Image-Guided and Intensity-Modulated Radiation Therapies Using Thermoplastic Immobilization Device , 2013 .

[39]  Charles D. Hansen,et al.  A Shot at Visual Vulnerability Analysis , 2015, Comput. Graph. Forum.

[40]  Urska Cvek,et al.  High-Dimensional Visualizations , 2002 .

[41]  James Agutter,et al.  Transactions on Visualization and Computer Graphics Design Activity Framework for Visualization Design , 2014 .

[42]  M Joan Saary,et al.  Radar plots: a useful way for presenting multivariate health care data. , 2008, Journal of clinical epidemiology.

[43]  G. Elisabeta Marai,et al.  Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization , 2018, IEEE Transactions on Visualization and Computer Graphics.

[44]  Jae-Seong Yang,et al.  OASIS: Online Application for the Survival Analysis of Lifespan Assays Performed in Aging Research , 2011, PloS one.

[45]  Douglas G Altman,et al.  Survival plots of time-to-event outcomes in clinical trials: good practice and pitfalls , 2002, The Lancet.

[46]  Donald D. Chamberlin,et al.  Functional specifications of a subsystem for data base integrity , 1975, VLDB '75.

[47]  Bernhard Preim,et al.  Interactive Visual Analysis of Heterogeneous Cohort-Study Data , 2014, IEEE Computer Graphics and Applications.