Evidence map›Paper›PMID 25538797›Full record

ReviewThe EPMA journal2014

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

Leonard Berliner, Heinz U Lemke, Eric vanSonnenberg, Hani Ashamalla, Malcolm D Mattes, David Dosik, Hesham Hazin, Syed Shah, Smruti Mohanty, Sid Verma and 6 more

Abstract readReview
In one paragraph

Review in The EPMA journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Leonard BerlinerNew York Methodist Hospital, Brooklyn, NY 11215, USA ; Weill Medical College of Cornell University, New York, NY 10021, USA.
Heinz U LemkeTechnical University of Berlin, 10623 Berlin, Germany ; University of Southern California, Los Angeles, CA, 90089, USA.
Eric vanSonnenbergDavid Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA ; University of Arizona College of Medicine, Tucson, AZ, 85724, USA.
Hani AshamallaNew York Methodist Hospital, Brooklyn, NY 11215, USA ; Weill Medical College of Cornell University, New York, NY 10021, USA.
Malcolm D MattesNew York Methodist Hospital, Brooklyn, NY 11215, USA.
David DosikNew York Methodist Hospital, Brooklyn, NY 11215, USA ; Weill Medical College of Cornell University, New York, NY 10021, USA.
Hesham HazinNew York Methodist Hospital, Brooklyn, NY 11215, USA.
Syed ShahNew York Methodist Hospital, Brooklyn, NY 11215, USA ; Weill Medical College of Cornell University, New York, NY 10021, USA.
Smruti MohantyNew York Methodist Hospital, Brooklyn, NY 11215, USA ; Weill Medical College of Cornell University, New York, NY 10021, USA.
Sid VermaNew York Methodist Hospital, Brooklyn, NY 11215, USA.
Giuseppe EspositoGeorgetown University Medical Center, Washington, DC 20007, USA.
Irene BargelliniUniversity Hospital Pisa, University of Pisa (I), 56126 Pisa, Italy.
Valentina BattagliaUniversity Hospital Pisa, University of Pisa (I), 56126 Pisa, Italy.
Davide CaramellaUniversity Hospital Pisa, University of Pisa (I), 56126 Pisa, Italy.
Carlo BartolozziUniversity Hospital Pisa, University of Pisa (I), 56126 Pisa, Italy.
Paul MorrisonBrigham and Women's Hospital, Boston, MA 02115, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Bayesian networkDigital patient modelHepatocellular carcinomaInformation technologyModel-based medical evidenceModel-guided therapyPatient-specific modelPersonalized medicineTherapy imaging and model management systemTIMMS

Identifiers

PMID25538797
PMCPMC4274760

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.