Leonard Berliner

Patients with hepatic breast cancer metastases demonstrate highly specific profiles of matrix metalloproteinases MMP-2 and MMP-9 after SIRT treatment as compared to other primary and secondary liver tumours

BackgroundPatients with primary and metastatic liver malignancies represent a highly heterogeneous patient pool characterised by some of the shortest life expectancies amongst oncology patients. Investigation and better understanding of liver malignancies is an emerging field which requires high-quality multidisciplinary research and collaboration.MethodsA study of 158 patients with primary hepatic carcinomas and secondary liver metastases, altogether 15 cancer types of different origin, who underwent selective internal radiation therapy (SIRT) with Yttrium90 or transarterial chemoembolisation, was undertaken in an effort to detect distinguishing features with respect to activity profiles of both blood matrix metalloproteinase (MMP-2 and MMP-9).ResultsNoteworthy, stratification of all hepatic cancer groups with respect to MMP-2 and MMP-9 activities revealed characteristic patterns specifically in patients with hepatic breast cancer metastases who had undergone SIRT. In contrast to all other groups, these patients demonstrated well-consolidated profiles of both MMPs, reflecting a common feature, namely an immediate and durable increase of their activity after the SIRT treatment. Although the total number of patients in the breast cancer group is relatively small (15 patients), since increased activities of MMP-2 and MMP-9 are well known prognostic factors for poor outcomes of oncologic patients, the significance and clear group-specificity (from 15 ones investigated here) of this previously unanticipated finding requires particular attention and further investigations. Particularly important is to determine, whether this increase of the metalloproteinase activity was provoked by SIRT, as well as whether special selection criteria are required for patients with breast cancer metastases to the liver who are being considered for SIRT.ConclusionsIt is recommended that a more focused, multidisciplinary and large-scaled investigations of the possible adverse effects of SIRT in patients with advanced metastatic disease of breast cancer be undertaken, with an appropriate patients’ stratification, set-up of the relevant patient profiles and disease modelling.

Model-guided therapy for hepatocellular carcinoma: a role for information technology in predictive, preventive and personalized medicine

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.

Specification and design of a Therapy Imaging and Model Management System (TIMMS)

Appropriate use of Information and Communication Technology (ICT) and Mechatronic (MT) systems is considered by many experts as a significant contribution to improve workflow and quality of care in the Operating Room (OR). This will require a suitable IT infrastructure as well as communication and interface standards, such as DICOM and suitable extensions, to allow data interchange between surgical system components in the OR. A conceptual design of such an infrastructure, i.e. a Therapy Imaging and Model Management System (TIMMS) will be introduced in this paper. A TIMMS should support the essential functions that enable and advance image, and in particular, patient model guided therapy. Within this concept, the image centric world view of the classical PACS technology is complemented by an IT model-centric world view. Such a view is founded in the special modelling needs of an increasing number of modern surgical interventions as compared to the imaging intensive working mode of diagnostic radiology, for which PACS was originally conceptualised and developed. A proper design of a TIMMS, taking into account modern software engineering principles, such as service oriented architecture, will clarify the right position of interfaces and relevant standards for a Surgical Assist System (SAS) in general and their components specifically. Such a system needs to be designed to provide a highly modular structure. Modules may be defined on different granulation levels. A first list of components (e.g. high and low level modules) comprising engines and repositories of an SAS, which should be integrated by a TIMMS, will be introduced in this paper.

Validation workflow for a clinical Bayesian network model in multidisciplinary decision making in head and neck oncology treatment

PurposeOncological treatment is being increasingly complex, and therefore, decision making in multidisciplinary teams is becoming the key activity in the clinical pathways. The increased complexity is related to the number and variability of possible treatment decisions that may be relevant to a patient. In this paper, we describe validation of a multidisciplinary cancer treatment decision in the clinical domain of head and neck oncology.MethodProbabilistic graphical models and corresponding inference algorithms, in the form of Bayesian networks, can support complex decision-making processes by providing a mathematically reproducible and transparent advice. The quality of BN-based advice depends on the quality of the model. Therefore, it is vital to validate the model before it is applied in practice.ResultsFor an example BN subnetwork of laryngeal cancer with 303 variables, we evaluated 66 patient records. To validate the model on this dataset, a validation workflow was applied in combination with quantitative and qualitative analyses. In the subsequent analyses, we observed four sources of imprecise predictions: incorrect data, incomplete patient data, outvoting relevant observations, and incorrect model. Finally, the four problems were solved by modifying the data and the model.ConclusionThe presented validation effort is related to the model complexity. For simpler models, the validation workflow is the same, although it may require fewer validation methods. The validation success is related to the model’s well-founded knowledge base. The remaining laryngeal cancer model may disclose additional sources of imprecise predictions.

Information and communication technology in personalized medicine: a clinical use-case for hepatocellular cancer

Scientific objectives This study explores ways in which the requirements and interrelationships between Personalized Medicine (PM), clinical medical practice, and basic medical research could be best served by information and communication technologies (ICT). To avoid the problems inherent in formulating ICT solutions in isolation, a use-case was developed employing hepatocellular carcinoma (HCC). The subject matter was approached from four separate, but interrelated, tasks: (1) review of current understanding and clinical practices relating to HCC; (2) propose an ICT system for dealing with the vast amount of information relating to HCC, including clinical decision support and research needs; (3) determine the ways in which a clinical liver cancer center can contribute to this ICT approach; and, (4) examine the enhancements and impact that the first three tasks, and therefore Personalized Medicine, will have on the management of HCC. The development of an IT System for Personalized Healthcare (ITS-PHC) for HCC will provide a comprehensive system to identify and then determine the relative value of the wide number of variables or Information Entities (IEs): (1) factors reflecting clinical assessment of the patient including functional status, liver function, degree of cirrhosis, and comorbidities; (2) factors reflecting tumor biology, at a molecular, genetic and anatomic level; (3) factors reflecting tumor burden and individual patient response; and (4) factors reflecting medical and operative treatments and their outcomes. Technological approaches It is our hypothesis, that if we can utilize patient-specific modeling techniques to generate valid Digital Patient Models (DPM) (utilizing these IEs) we may be able to develop a statistically valid methodology for predicting diseases, predicting treatment outcomes, preventing diseases or complications, and developing personalized treatment regimens. We are calling this proposed system Model-Based Medical Evidence (MBME), and as yet remains undeveloped. It is further postulated that MultiEntity Bayesian Networks (MEBN) used in the construction of the DPM will be utilized in the development of a practical decision support system. Literature regarding HCC was analyzed, combining epidemiology; risk factors; infectious etiologies; pathology, microenvironment and biomarkers; screening and diagnostic technologies; treatment modalities. IEs, that will be used to populate the patient databases and MEBNs required for data mining and decision support, were identifed. This information was also used to reinforce a well-established treatment protocol, i.e. the Barcelona treatment algorithm, and, to add extensions that include enhanced screening and greater specifics regarding treatment selections.

The Digital Patient Model and Model Guided Therapy

One of the goals of this book is to provide a roadmap for the development of information technology (IT) tools to facilitate Predictive, Preventive, and Personalized Medicine (PPPM). Our approach to the management of medical information is based on model theory that has arisen from a conceptual transformation from image-guided patient management to a model-centric world-view or model-guided patient management. This approach seeks to implement a comprehensive form of Model-Guided Therapy (MGT) through the use of a Therapy Imaging and Model Management System (TIMMS), and its application as a decision support system for achieving MGT. It is our hypothesis that if we can utilize patient-specific modeling techniques to generate valid Digital Patient Models (DPMs) we may be able to develop a statistically valid methodology for predicting diseases and treatment outcomes, preventing diseases or complications, and developing personalized treatment regimens. We are calling this proposed system Model-Based Medical Evidence (MBME) and are engaged in its development. It is further postulated that Multi-Entity Bayesian Networks (MEBN) used in the construction of the DPM will be utilized in the development of a practical decision support system.

Hepatocellular Carcinoma and Patient Assessment

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and the fifth most common cancer worldwide. Due to its aggressive nature and poor survival rate, incidence and mortality rates are almost equivalent, accounting for approximately 500,000 deaths annually. Since HCC is most often seen in patients with chronic liver disease or cirrhosis, the incidence of HCC and the epidemiology of underlying liver diseases are closely linked. This Chapter provides a review of the literature regarding HCC including epidemiology; risk factors; infectious etiologies; pathology, microenvironment, and biomarkers; screening and diagnostic technologies; and treatment modalities. The information accumulated in this review will be used to generate the information entities (IEs) that will be used to populate the patient databases and Multi-Entity Bayesian Networks (MEBNs) required for generating Digital Patient Models (DPMs) to facilitate data mining and decision support. For any given patient with HCC, the DPM will need to be continuously updated to ensure appropriate guidance of the patient throughout the course of their disease.

The digital operating room: towards intelligent infrastructures and processes

Based on current research and development activities, a timeline with five stages of maturity levels for the development of the Digital Operating Room (DOR) during the first quarter of the twenty-first century will be outlined. In particular, there are several areas of technology development for the DOR such as (1) Devices, including signal detection and recording, robotics, navigation systems and simulation technologies, which allow more precision in the delivery of personalized interventional therapy; (2) Information and Communication Technology (ICT) Infrastructure and Standards, including Digital Imaging and Communications in Medicine (DICOM), Integrating the Healthcare Enterprise (IHE), the electronic medical record (EMR), and Therapy Imaging and Model Management System (TIMMS) infrastructure for the storage, integration, processing and transmission of patient specific data in and outside the operating room; and (3) Functionalities, including patient specific modeling for selected interventional processes, optimization of surgical workflow as well as TIMMS engines and repositories for improving the overall quality of surgical interventions. Patient specific modeling, work flow management and standards are key aspects for the development of DOR technologies. They will be the prerequisite for intelligent infrastructures and processes in the digital operating room of the future. Architectural aspects of an intelligent infrastructure, specifically a Therapy Imaging and Model Management System (TIMMS) and the Patient-Specific Model (PSM) as well as Standards and integration mechanisms are therefore briefly discussed in this paper.

Surgical Treatment for Hepatocellular Carcinoma

The treatment of hepatocellular carcinoma (HCC) has undergone evolution and refinement over the past three decades. Changes in the understanding of HCC with respect to tumor size, number and location, underlying liver function and portal pressure, and hepatic anatomy, in combination with refinement of surgical techniques and technologies, have greatly influenced the approach to surgical management. Surgery is considered the mainstay of curative HCC treatment with resection and transplantation achieving the best outcomes in well-selected candidates (5-year survival of 60–80 %). Surgical resection of HCC, especially within the Milano/Mazzaferro criteria (i.e., solitary tumor ≤ 5 cm or up to three tumors all ≤ 3 cm) in patients with well-preserved liver function (Child-Pugh A and selectively B patients), offers the greatest chances for survival. Liver transplantation is considered the treatment of choice for patients with compromised liver function (Child-Pugh B/C). The clinical parameters identified in this Chapter will be used to generate Digital Patient Models (DPMs) to facilitate diagnosis, prognosis, and treatment selection, i.e. Model Guided Therapy (MGT). The following have been identified as key issues relating to Predictive, Preventive, and Personalized Medicine (PPPM) and surgical treatment for HCC: tumor characterization, such as size, number, and vascular invasion; the patientʼs clinical status, particularly the presence of cirrhosis, the degree of portal hypertension, and liver functional reserve; pre-operative management, such as patient selection for resection or transplantation, choice of donor, down-staging and bridging therapies; and, surgical techniques, including techniques to minimize blood loss and to ensure an adequate liver remnant.

Minimally Invasive Therapies for Hepatocellular Cancer: Catheter-Directed Therapies

Techniques have been developed for catheter-directed delivery of therapy for hepatocellular carcinoma (HCC) since the 1980s, and are still undergoing evolution. Currently, this involves embolization with particles, as well as delivery of chemotherapeutic agents with a variety of materials, and is referred to as transarterial chemoembolization (TACE). TACE is made both feasible and effective due to the dual blood supply of the liver. Advances in catheter and guide wire technology have been accompanied by the development of techniques for the superselective placement of catheters for the safe and effective delivery of therapeutic agents to hepatic tumors. TACE is recommended for patients with Intermediate Stage, multi-nodular HCC (Okuda Stage 1–2; Childs-Pugh Stage A-B; Performance Status 0). Combination therapy with RFA and TACE may lead to more extensive tumor necrosis than mono-ablative therapy and may be a more effective treatment for HCC. Further study will be needed to determine the effectiveness of combining RFA and TACE, and in which order. The combination of TACE with antiangiogenic agents, such as sorafenib, is under investigation as well. The use of sorafenib may curtail the post-TACE rise in VEGF-mediated signaling, and simultaneously target tumor foci distant from the site of treatment. Selection parameters and treatment outcomes for locoregional therapies, alone or in combination, such as thermal ablation and TACE, with or without systemic chemotherapy agents will eventually be factored in generating Digital Patient Models (DPMs) to facilitate diagnosis, prognosis, and treatment selection, i.e. Model Guided Therapy (MGT) and Predictive, Preventive and Personalized Medicine (PPPM).