Evidence map›Paper›PMID 25843738›Full record

ArticleSeminars in oncology2015

Evaluation of total hepatocellular cancer lifespan, including both clinically evident and preclinical development, using combined network phenotyping strategy and fisher information analysis.

Petr Pančoška, Lubomír Skála, Jaroslav Nešetřil, Brian I Carr

Abstract read
In one paragraph

Article in Seminars in oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

4 authors.

Petr PančoškaDepartment of Medicine and Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA; Computer Science Institute (IUUK) of Charles University Prague, Czech Republic.
Lubomír SkálaDepartment of Chemical Physics and Optics, Faculty of Mathematics and Physics, Charles University Prague, Czech Republic.
Jaroslav NešetřilComputer Science Institute (IUUK) of Charles University Prague, Czech Republic.
Brian I CarrDepartment of Gastroenterology and Liver Diseases Tel-Aviv Sourasky Medical Center Tel Aviv, Israel. Electronic address: brianicarr@hotmail.com.

Funding

K VITAMINS--A NEW CLASS OF LIVER CELL GROWTH INHIBITORSR01CA082723 · NCI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CARR, BRIAN I · 2000 to 2008
$2.3M
NCI NIH HHS CA 82723NCI NIH HHS R01 CA082723
6 · The paper itself

Abstract

We previously showed that for hepatocellular cancer (HCC) prognostication, disease parameters need to be considered within a total personal clinical context. This requires preserving the coherence of data values, observed simultaneously for each patient during baseline diagnostic evaluation. Application of the Network Phenotyping Strategy (NPS) provided quantitative descriptors of these patient coherences. Combination of these descriptors with Fisher information about the patient tumor mass and the histogram of the tumor masses in the whole cohort permitted estimation of the time from disease onset until clinical diagnosis (t(baseline)). We found faster growth of smaller tumors having total masses<70 (80% of cohort) which involved about three times more interacting cellular processes than were observed for slower growing larger tumors (20% of cohort) with total masses>70. Combining the clinical survival and t(baseline) normalized all HCC patients to a common 1,045 days of mean total disease duration (t(baseline) plus post diagnosis survival). We also found a simple relationship between the baseline clinical status, t(baseline), and survival. Every difference between individual patient baseline clinical profiles and special coherent clinical status (HL1) reduced the above common overall survival (OVS) by 65 days. In summary, we showed that HCC patients with any given tumor can best have their tumor biology understood, when account is taken of the total clinical and liver contexts, and with knowing the point in the tumor history when an HCC diagnosis is made. This ability to compute the t(baseline) from standard clinical data brings us closer to calculating survival from diagnosis of individual HCC patients.

Indexed as

Models, TheoreticalAdultAgedCarcinoma, HepatocellularFemaleHumansLiver NeoplasmsMaleMiddle AgedPrognosis

Identifiers

PMID25843738
PMCPMC4388062

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Registered trials

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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.