Evidence map›Paper›PMID 30089770›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2018

Use of Receiver Operating Characteristic (ROC) Curve Analysis for Tyrer-Cuzick and Gail in Breast Cancer Screening in Jiangxi Province, China.

Le Zhang, Zhigang Jie, Shengxi Xu, Liqun Zhang, Xiangqu Guo

Abstract read
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Article in Medical science monitor : international medical journal of experimental and clinical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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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

8 citing papers in PubMed.

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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

5 authors.

Le ZhangDepartment of Galactophore, Affiliated Jiujiang Hospital of Nanchang University, Jiujiang, Jiangxi, China (mainland).
Zhigang JieDepartment of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China (mainland).
Shengxi XuDepartment of Galactophore, Affiliated Jiujiang Hospital of Nanchang University, Jiujiang, Jiangxi, China (mainland).
Liqun ZhangDepartment of Galactophore, Affiliated Jiujiang Hospital of Nanchang University, Jiujiang, Jiangxi, China (mainland).
Xiangqu GuoDepartment of Galactophore, Affiliated Jiujiang Hospital of Nanchang University, Jiujiang, Jiangxi, China (mainland).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND Breast cancer is a malignant tumor derived from breast gland epithelium. The screening and early diagnosis of breast cancer in high-risk populations can effectively suppress its threat to women's health and improve treatment efficiency, and thus has critical importance. Using various evaluation models, the present study evaluated cancer risk in 35-69-year-old women, and the usefulness of models in breast cancer prevention was compared. MATERIAL AND METHODS A total of 150 infiltrative breast cancer patients who were diagnosed with breast cancer at our hospital were recruited, along with 130 healthy women as the control group. A retrospective study was performed to collect information. The 5-year risk of breast cancer was evaluated using the Gail and Tyrer-Cuzick models. Diagnostic results were analyzed to plot ROC curves for comparing the value for screening between Gail and Tyrer-Cuzick models. RESULTS The Gail model has 53.33% sensitivity and 77.69% specificity, with 73.39% positive prediction value, 59.06% negative prediction value, 64.64% accuracy, and 0.31 Jordon index. The Tyrer-Cuzick model had 66.00% sensitivity, 86.92% specificity, 85.34% positive prediction value, 68.90% negative prediction value, 75.71% accuracy, and 0.53 Jordon index. The area under the curve (AUC) was 0.665 for the Gail model (95% CI: 0.629~0.701) and 0.786 for the Tyrer-Cuzick model (95% CI: 0.757~0.815). CONCLUSIONS Both Gail model and Tyrer-Cuzick models can be used to evaluate breast cancer risk. The Gail model has relatively lower accuracy in evaluating breast cancer risk in Jiangxi province of China and the Tyrer-Cuzick model had relatively higher accuracy.

Indexed as

AdultAgedArea Under CurveBreast NeoplasmsChinaEarly Detection of CancerFemaleHumansMiddle AgedModels, StatisticalRetrospective StudiesRisk AssessmentRisk FactorsROC CurveSensitivity and Specificity

Identifiers

PMID30089770
PMCPMC6097135

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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.