Evidence map›Paper›PMID 33007594›Full record

ArticleComputer methods and programs in biomedicine2020

Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients.

Xuxin Chen, Wei Liu, Theresa C Thai, Tara Castellano, Camille C Gunderson, Kathleen Moore, Robert S Mannel, Hong Liu, Bin Zheng, Yuchen Qiu

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

Article in Computer methods and programs in biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 10% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.

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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

10 authors at 2 institutions in 2 countries.

Xuxin ChenSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.
Wei LiuSchool of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710021, China; School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA.
Theresa C ThaiDepartment of Radiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA.
Tara CastellanoDepartment of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA.
Camille C GundersonDepartment of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA.
Kathleen MooreDepartment of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, USA.
Robert S MannelDepartment of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, 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.
Yuchen QiuSchool of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA. Electronic address: QIUYUCHEN@OU.EDU.
University of Oklahoma · USUniversity of Oklahoma Health Sciences Center · US

Funding

Tracking and Evaluation CoreU54GM104938 · NIGMS · UNIVERSITY OF OKLAHOMA HLTH SCIENCES CTR · PI TIMOTHY M VANWAGONER · 2013 to 2026
$68.2M
Increasing Cancer Detection Yield Using Breast MRI Screening ModalityR01CA197150 · NCI · UNIVERSITY OF OKLAHOMA · PI HOLLINGSWORTH, ALAN B., LIU, HONG · 2015 to 2019
$2.5M
NCI NIH HHS R01 CA197150NIGMS NIH HHS U54 GM104938
6 · The paper itself

Abstract

BACKGROUND AND

objectiveIn diagnosis of cervical cancer patients, lymph node (LN) metastasis is a highly important indicator for the following treatment management. Although CT/PET (i.e., computed tomography/positron emission tomography) examination is the most effective approach for this detection, it is limited by the high cost and low accessibility, especially for the rural areas in the U.S.A. or other developing countries. To address this challenge, this investigation aims to develop and test a novel radiomics-based CT image marker to detect lymph node metastasis for cervical cancer patients.

methodsA total of 1,763 radiomics features were first computed from the segmented primary cervical tumor depicted on one CT image with the maximal tumor region. Next, a principal component analysis algorithm was applied on the initial feature pool to determine an optimal feature cluster. Then, based on this optimal cluster, the prediction models (i.e., logistic regression or support vector machine) were trained and optimized to generate an image marker to detect LN metastasis. In this study, a retrospective dataset containing 127 cervical cancer patients were established to build and test the model. The model was trained using a leave-one-case-out (LOCO) cross-validation strategy and image marker performance was evaluated using the area under receiver operation characteristic (ROC) curve (AUC).

resultsThe results indicate that the SVM based imaging marker achieved an AUC value of 0.841 ± 0.035. When setting an operating threshold of 0.5 on model-generated prediction scores, the imaging marker yielded a positive and negative predictive value (PPV and NPV) of 0.762 and 0.765 respectively, while the total accuracy is 76.4%.

conclusionsThis study initially verified the feasibility of utilizing CT image and radiomics technology to develop a low-cost image marker to detect LN metastasis for assisting stratification of cervical cancer patients.

Indexed as

Uterine Cervical NeoplasmsFemaleHumansLymphatic MetastasisLymph NodesPositron Emission Tomography Computed TomographyRetrospective StudiesTomography, X-Ray ComputedComputer aided detectionLymph node metastasisRadiomics Cervical cancerTreatment management

Identifiers

PMID33007594
PMCPMC7796823
OpenAlexW3090937149

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.