Evidence map›Paper›PMID 30952823›Full record

ArticleThe oncologist2019

Prediction of Bone Metastasis in Inflammatory Breast Cancer Using a Markov Chain Model.

Takeo Fujii, Jeremy Mason, Angela Chen, Peter Kuhn, Wendy A Woodward, Debu Tripathy, Paul K Newton, Naoto T Ueno

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The oncologist, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 38% of its field
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

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Application of Markov models to predict changes in nasal carriage ofJournal of occupational and environmental hygiene · 2022
    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

8 authors at 2 institutions in 1 country.

Takeo FujiiDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Jeremy MasonDepartments of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, Los Angeles, California, USA.
Angela ChenDepartments of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, Los Angeles, California, USA.
Peter KuhnDepartments of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, Los Angeles, California, USA.
Wendy A WoodwardMorgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Debu TripathyDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Paul K NewtonMathematics, Dornsife College of Letters, Arts, and Sciences, University of Southern California, Los Angeles, California, USA nueno@mdanderson.org newton@usc.edu.
Naoto T UenoDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA nueno@mdanderson.org newton@usc.edu.
The University of Texas MD Anderson Cancer Center · USUniversity of Southern California · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInflammatory breast cancer (IBC) is a rare yet aggressive variant of breast cancer with a high recurrence rate. We hypothesized that patterns of metastasis differ between IBC and non-IBC. We focused on the patterns of bone metastasis throughout disease progression to determine statistical differences that can lead to clinically relevant outcomes. Our primary outcome of this study is to quantify and describe this difference with a view to applying the findings to clinically relevant outcomes for patients. SUBJECTS, MATERIALS, AND

methodsWe retrospectively collected data of patients with nonmetastatic IBC (

resultsIn patients with IBC, the probabilities of developing bone metastasis after chest wall, lung, or liver metastasis as the first site of progression were high: 28%, 21%, and 21%, respectively. For patients with non-IBC, the probability of developing bone metastasis was fairly consistent regardless of initial metastasis site.

conclusionMetastatic patterns of spread differ between patients with IBC and non-IBC. Selection of patients with IBC with known liver, chest wall, and/or lung metastasis would create a population in whom to investigate effective methods for preventing future bone metastasis. IMPLICATIONS FOR PRACTICE: This study demonstrated that the patterns of metastasis leading to and following bone metastasis differ significantly between patients with inflammatory breast cancer (IBC) and those with non-IBC. Patients with IBC had a progression pattern that tended toward the development of bone metastasis if they had previously developed metastases in the liver, chest wall, and lung, rather than in other sites. Selection of patients with IBC with known liver, chest wall, and/or lung metastasis would create a population in whom to investigate effective methods for preventing future bone metastasis.

Indexed as

Bone NeoplasmsFemaleHumansInflammatory Breast NeoplasmsMarkov ChainsMiddle AgedRetrospective StudiesBone metastasisBreast cancerInflammatory breast cancerMarkov chain modelPrediction model

Identifiers

PMID30952823
PMCPMC6795167
OpenAlexW2930653204

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.