Evidence map›Paper›PMID 25851056›Full record

ArticleBiometrics2015

Predictive classification of correlated targets with application to detection of metastatic cancer using functional CT imaging.

Yuan Wang, Brian P Hobbs, Jianhua Hu, Chaan S Ng, Kim-Anh Do

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.4field-weighted citation impact, top 20% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

5 authors at 1 institution in 1 country.

Yuan WangDepartment of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A.
Brian P HobbsDepartment of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A.
Jianhua HuDepartment of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A.
Chaan S NgDepartment of Diagnostic Radiology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A.
Kim-Anh DoDepartment of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, Texas, U.S.A.
The University of Texas MD Anderson Cancer Center · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Targeting Tumor Microenvironment-Induced Therapy Resistance in Prostate Cancer Bone MetastasisP50CA140388 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LOGOTHETIS, CHRISTOPHER J., THOMPSON, TIMOTHY CHARLES · 2009 to 2022
$23.4M
Consistent variable selection in p>>n settingsR01CA158113 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI JOHNSON, VALEN EARL · 2011 to 2020
$2.8M
Statistical Methods and Software for More Efficient, Ethical, and Affordable ClinR01CA157458 · NCI · UNIVERSITY OF MINNESOTA · PI CARLIN, BRADLEY P · 2012 to 2014
$939k
NCI NIH HHS P30 CA016672NCI NIH HHS P30-CA016672NCI NIH HHS P50 CA140388NCI NIH HHS P50-CA140388NCI NIH HHS R01 CA157458NCI NIH HHS R01-CA157458NCI NIH HHS R01 CA158113NCI NIH HHS R01-CA158113
6 · The paper itself

Abstract

Perfusion computed tomography (CTp) is an emerging functional imaging modality that uses physiological models to quantify characteristics pertaining to the passage of fluid through blood vessels. Perfusion characteristics provide physiological correlates for neovascularization induced by tumor angiogenesis. Thus CTp offers promise as a non-invasive quantitative functional imaging tool for cancer detection, prognostication, and treatment monitoring. In this article, we develop a Bayesian probabilistic framework for simultaneous supervised classification of multivariate correlated objects using separable covariance. The classification approach is applied to discriminate between regions of liver that contain pathologically verified metastases from normal liver tissue using five perfusion characteristics. The hepatic regions tend to be highly correlated due to common vasculature. We demonstrate that simultaneous Bayesian classification yields dramatic improvements in performance in the presence of strong correlation among intra-subject units, yet remains competitive with classical methods in the presence of weak or no correlation.

Indexed as

Models, StatisticalAngiographyBayes TheoremComputer SimulationData Interpretation, StatisticalFemaleHumansLiver NeoplasmsMaleMiddle AgedNeoplasm MetastasisRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsSensitivity and SpecificityStatistics as TopicTomography, X-Ray ComputedBayesian decision analysisCancer detectionMetastatic liver cancerPerfusion imagingSpatial correlation

Identifiers

PMID25851056
PMCPMC4575264
OpenAlexW1497170325

What OpenQuestion holds

Textmetadata
LicenceTDM
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.