What happens when we have data?

Background: Treatment recommendations (guidelines) are commonly represented in text form. Based on parameters (questions) recommendations are defined (answers). Objectives: To improve handling, alternative forms of representation are required. Methods: The concept of Dodes (diagnostic nodes) has been developed. Dodes contain answers and questions. Dodes are based on linked nodes and additionally contain descriptive information and recommendations. Dodes are organized hierarchically into Dode trees. Dode categories must be defined to prevent redundancy. Results: A centralized and neutral Dode database can provide standardization, which is a requirement for the comparison of recommendations. Centralized administration of Dode categories can provide information about diagnostic criteria (Dode categories) underutilized in existing recommendations (Dode trees). Conclusions: Representing clinical recommendations in Dode trees improves their manageability, handling and updateability

Developing a tool for crowd-sourcing to Verify a Radiation Oncology Ontology: a Summer Project

We have been unable to find a verified, published Radiation Oncology Ontology. We undertook the process of verifying a Radiation Oncology Ontology with a mixture of crowd-sourcing and expert-based approaches to verify relationships in the ontology. We used a natural language based approach to portray concepts and relationships, surveying users to assess the relationships between concepts in the Radiation Oncology ontology. The work used a description of a patient\u27s history expressed in XML. The natural language statements relating concepts are available on a website for verification, and readers are invited to complete the survey at http://coi-hs-survey.appspot.com/ to contribute

A Graphical Tool and Methods for Assessing Margin Definition From Daily Image Deformations

Estimating the proper margins for the planning target volume (PTV) could be a challenging task in cases where the organ undergoes significant changes during the course of radiotherapy treatment. Developments in image-guidance and the presence of onboard imaging technologies facilitate the process of correcting setup errors. However, estimation of errors to organ motions remain an open question due to the lack of proper software tools to accompany these imaging technological advances. Therefore, we have developed a new tool for visualization and quantification of deformations from daily images. The tool allows for estimation of tumor coverage and normal tissue exposure as a function of selected margin (isotropic or anisotropic). Moreover, the software allows estimation of the optimal margin based on the probability of an organ being present at a particular location. Methods based on swarm intelligence, specifically Ant Colony Optimization (ACO) are used to provide an efficient estimate of the optimal margin extent in each direction. ACO can provide global optimal solutions in highly nonlinear problems such as margin estimation. The proposed method is demonstrated using cases from gastric lymphoma with daily TomoTherapy megavoltage CT (MVCT) contours. Preliminary results using Dice similarity index are promising and it is expected that the proposed tool will be very helpful and have significant impact for guiding future margin definition protocols

The Informatics of the Planning Target Volume -- why it cannot and should not be changed

The ICRU defined the Planning Target Volume (PTV) as a static and geometrical volume in 1993. Radiation oncologists continue to manually alter PTVs in their daily practice when critical organs at risk (OAR) are too close to high dose PTVs. This practice is examined and shown to be non-standard (defies the ICRU definitions), inaccurate (all DVHs look perfect when the plan is manifestly NOT perfect), and useless for outcomes research (automatically analysed DVHs will find situations where the PTV_unaltered overlaps the OAR_unaltered, but will fail to find situations where an OAR_unaltered would be overlapped by a PTV_unaltered, but is not overlapped by a PTV_altered

A Rational Informatics-enabled approach to Standardised Nomenclature of Contours and Volumes in Radiation Oncology Planning

The standardising of nomenclature in the radiotherapy planning process has deep implications for the abilityof the profession to examine the adequacy of construction of radiotherapy plans in outcomes research, particularly in relation to disease control and toxicity generation. This paper proposes an interim standardisednomenclature which can be used by any institution as a template for a mappable local standard.The nomenclature is systematically constructed using the Foundational Model of Anatomy, ICRU Report 50 and ICRU report 62. The system foreshadows a XML metadata structure to detail the method of constructionof volumes. Treatment Planning System vendors should build their software with the ability to use this systematic construction technique so that contours and volumes in a radiotherapy plan can be annotated. Thismetadata will allow the investigation of how a radiation plan\u27s construction can affect the therapy outcome

Towards Prediction of Radiation Pneumonitis Arising from Lung Cancer Patients Using Machine Learning Approaches

Radiation pneumonitis (RP) is a potentially fatal side effect arising in lung cancer patients who receive radiotherapy as part of their treatment. For the modeling of RP outcomes data, several predictive models based on traditional statistical methods and machine learning techniques have been reported. However, no guidance to variation in performance has been provided to date. In this study, we explore several machine learning algorithms for classification of RP data. The performance of these classification algorithms is investigated in conjunction with several feature selection strategies and the impact of the feature selection strategy on performance is further evaluated. The extracted features include patients demographic, clinical and pathological variables, treatment techniques, and dose-volume metrics. In conjunction, we have been developing an in-house Matlab-based open source software tool, called DREES, customized for modeling and exploring dose response in radiation oncology. This software has been upgraded with a popular classification algorithm called support vector machine (SVM), which seems to provide improved performance in our exploration analysis and has strong potential to strengthen the ability of radiotherapy modelers in analyzing radiotherapy outcomes data