| Literature DB >> 25538797 |
Leonard Berliner1, Heinz U Lemke2, Eric vanSonnenberg3, Hani Ashamalla1, Malcolm D Mattes4, David Dosik1, Hesham Hazin4, Syed Shah1, Smruti Mohanty1, Sid Verma4, Giuseppe Esposito5, Irene Bargellini6, Valentina Battaglia6, Davide Caramella6, Carlo Bartolozzi6, Paul Morrison7.
Abstract
Predictive, preventive and personalized medicine (PPPM) may have the potential to eventually improve the nature of health care delivery. However, the tools required for a practical and comprehensive form of PPPM that is capable of handling the vast amounts of medical information that is currently available are currently lacking. This article reviews a rationale and method for combining and integrating diagnostic and therapeutic management with information technology (IT), in a manner that supports patients through their continuum of care. It is imperative that any program devised to explore and develop personalized health care delivery must be firmly rooted in clinically confirmed and accepted principles and technologies. Therefore, a use case, relating to hepatocellular carcinoma (HCC), was developed. The approach to the management of medical information we have taken is based on model theory and seeks to implement a form of model-guided therapy (MGT) that can be used as a decision support system in the treatment of patients with HCC. The IT structures to be utilized in MGT include a therapy imaging and model management system (TIMMS) and a digital patient model (DPM). The system that we propose will utilize patient modeling techniques to generate valid DPMs (which factor in age, physiologic condition, disease and co-morbidities, genetics, biomarkers and responses to previous treatments). We may, then, be able to develop a statistically valid methodology, on an individual basis, to predict certain diseases or conditions, to predict certain treatment outcomes, to prevent certain diseases or complications and to develop treatment regimens that are personalized for that particular patient. An IT system for predictive, preventive and personalized medicine (ITS-PM) for HCC is presented to provide a comprehensive system to provide unified access to general medical and patient-specific information for medical researchers and health care providers from different disciplines including hepatologists, gastroenterologists, medical and surgical oncologists, liver transplant teams, interventional radiologists and radiation oncologists. The article concludes with a review providing an outlook and recommendations for the application of MGT to enhance the medical management of HCC through PPPM.Entities:
Keywords: Bayesian network; Digital patient model; Hepatocellular carcinoma; Information technology; Model-based medical evidence; Model-guided therapy; Patient-specific model; Personalized medicine; TIMMS; Therapy imaging and model management system
Year: 2014 PMID: 25538797 PMCID: PMC4274760 DOI: 10.1186/1878-5085-5-16
Source DB: PubMed Journal: EPMA J ISSN: 1878-5077 Impact factor: 6.543
Figure 1A generic PSM template. The DPM is generated when the specific factors and values are entered into the full set of templates (one for each organ system) and the output is analyzed and may be used for prediction.
Figure 2The structure of the therapy imaging and model management system (TIMMS). The TIMMS may provide much of the IT framework for personalized medicine. The Kernel for workflow and knowledge and decision management, the patient-specific models repository and the process models repository are the central components for the development of digital patient models through the process of patient-specific modeling. ICT information and communication technology; WF workflow; K + D knowledge and decision; Rep representation; Mechatr mechatronic.
Figure 3A schematic for organization of an ITS-PM. This diagram reorganizes many of the TIMMS components in a structure that will enable the secure interchange of information between data sources, database management systems, data analysis systems and end-user applications. (PSM patient-specific model; TIMMS therapy and imaging model management system; PACS picture archiving and communications system; MEBN multi-entity Bayesian network; NoSQL not only structured query language; DBs databases.
Figure 4The Barcelona Clinic Liver Cancer (BCLC) staging system for hepatocellular carcinoma, revised 2011. PST performance status; CLT/LDLT cadaver liver transplant/living donor liver transplant; RF/PEI radiofrequency ablation/percutaneous ethanol injection; TACE transarterial chemoembolization [29].
Radiological features that may be employed as information entities
| Preprocedural assessment | (a) Number and size of HCC nodules; (b) number and size of nodules considered at risk for neoplastic degeneration; (c) presence and extension of portal vein neoplastic thrombosis; (d) presence of extrahepatic tumor spread; (e) radiological signs of cirrhosis (including varices and ascites); (f) biliary dilatations; (g) radiological signs of co-morbidities |
| Treatment planning | (a) Features of nodules such as location, degree of vascularization and presence of pseudocapsule; (b) vascular mapping; (c) technical details of previous treatments |
| Evaluation of previous treatment | (a) Complications; (b) tolerability and compliance; (c) radiological response |
Figure 5A portion of a simplified entity-relationship diagram for a relational database is shown displaying the 1st-, 2nd- and 3rd-order information entities of a generic patient-specific model and a 4th-order entity: hepatocellular carcinoma. INT integer; VARCHAR includes text [characters, numbers and punctuation].
Figure 6A portion of a simplified entity-relationship diagram for a relational database is shown displaying the 5th-order information entities relating to hepatocellular carcinoma.
Figure 7A portion of a simplified entity-relationship diagram for a relational database that may be linked to a graph database for research in biomarker and targeted therapies is shown displaying the 5th-order information entities relating to biomarkers and targeted therapies for HCC.
Figure 8It may be possible to build on the BCLC staging system platform with an IT system for personalized medicine (ITS-PM). The first goal will be, through enhanced screening, to have patients seek medical attention earlier in the course of their disease, so that they may enter the algorithm at a more favorable stage; i.e. a ‘shift to the left’. The second goal will be to improve outcomes through a better understanding of treatment subcategories, combined treatments and the effects of down-staging.
Figure 9A wider and more flexible assortment of alternative therapies and bridging therapies are introduced in this algorithm. This algorithm continues in Figures 10 and 11 (alternative therapy charts: single and multiple lesions).
Figure 10Alternative therapy chart: single lesions.
Figure 11Alternative therapy chart: multiple lesions.