Evidence map›Paper›PMID 27581075›Full record

ArticleBMC medical imaging2016

Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome.

Yunzhi Wang, Yuchen Qiu, Theresa Thai, Kathleen Moore, Hong Liu, Bin Zheng

Abstract read

In one paragraph

Article in BMC medical imaging, 2016. 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

–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

4 citing papers in PubMed.

Deep Learning-Based Body Composition Analysis for Cancer Patients Using Computed Tomographic Imaging.Journal of imaging informatics in medicine · 2025
Article
CT-quantified muscle and fat change in patients after surgery or endoscopic resection for early gastric cancer and its impact on long-term outcomes.Medicine · 2018
Observational
A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images.Computer methods and programs in biomedicine · 2017
Article
Computer-aided classification of mammographic masses using visually sensitive image features.Journal of X-ray science and technology · 2017
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

6 authors.

Yunzhi WangSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA. yunzhi.wang-1@ou.edu.

Yuchen QiuSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.

Theresa ThaiHealth Science Center of University of Oklahoma, Oklahoma City, OK, 73104, USA.

Kathleen MooreHealth Science Center of University of Oklahoma, Oklahoma City, OK, 73104, USA.

Hong LiuSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.

Bin ZhengSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.

Funding

Increasing Cancer Detection Yield Using Breast MRI Screening ModalityR01CA197150 · NCI · UNIVERSITY OF OKLAHOMA · PI HOLLINGSWORTH, ALAN B., LIU, HONG · 2015 to 2019

$2.5M

EPA EP-D-15-016NCI NIH HHS R01 CA197150

6 · The paper itself

Abstract

backgroundTo investigate the feasibility of automated segmentation of visceral and subcutaneous fat areas from computed tomography (CT) images of ovarian cancer patients and applying the computed adiposity-related image features to predict chemotherapy outcome.

methodsA computerized image processing scheme was developed to segment visceral and subcutaneous fat areas, and compute adiposity-related image features. Then, logistic regression models were applied to analyze association between the scheme-generated assessment scores and progression-free survival (PFS) of patients using a leave-one-case-out cross-validation method and a dataset involving 32 patients.

resultsThe correlation coefficients between automated and radiologist's manual segmentation of visceral and subcutaneous fat areas were 0.76 and 0.89, respectively. The scheme-generated prediction scores using adiposity-related radiographic image features significantly associated with patients' PFS (p < 0.01).

conclusionUsing a computerized scheme enables to more efficiently and robustly segment visceral and subcutaneous fat areas. The computed adiposity-related image features also have potential to improve accuracy in predicting chemotherapy outcome.

Indexed as

Abdominal FatAntineoplastic AgentsDisease-Free SurvivalDrug TherapyFeasibility StudiesFemaleHumansImage Interpretation, Computer-AssistedLogistic ModelsOvarian NeoplasmsRetrospective StudiesSurvival AnalysisTreatment OutcomeAntineoplastic AgentsClinical image markers for cancer prognosis predictionComputer-aided detection (CAD)Prediction of chemotherapy outcomeQuantitative image feature analysis

Identifiers

PMID27581075

DOI10.1186/s12880-016-0157-5

PMCPMC5006425

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LicenceCC BY

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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.