Comparison of the Diagnostic Performances of CA125 and Multiple Marker Combinations for Early Diagnosis of Ovarian Cancer

Song, Hye-Jeong; Yim, Jong-Ki; Park, Chan-Young; Kim, Yu-Seop; Kim, Jong-Dae

doi:10.14257/ijbsbt.2013.5.5.15

Cited by 3 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AUC has been applied to cancer diagnosis in several studies [18][19][20][21]. In this paper, AUC serves as the evaluation criterion for both diagnostic values of individual markers and performance of predictive models.…”

Section: Methods 21 Univariate Evaluation Criterionmentioning

confidence: 99%

Model‐Independent Evaluation of Tumor Markers and a Logistic‐Tree Approach to Diagnostic Decision Support

Huang

et al. 2014

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Sensitivity and specificity of using individual tumor markers hardly meet the clinical requirement. This challenge gave rise to many efforts, e.g., combing multiple tumor markers and employing machine learning algorithms. However, results from different studies are often inconsistent, which are partially attributed to the use of different evaluation criteria. Also, the wide use of model-dependent validation leads to high possibility of data overfitting when complex models are used for diagnosis. We propose two model-independent criteria, namely, area under the curve (AUC) and Relief to evaluate the diagnostic values of individual and multiple tumor markers, respectively. For diagnostic decision support, we propose the use of logistic-tree which combines decision tree and logistic regression. Application on a colorectal cancer dataset shows that the proposed evaluation criteria produce results that are consistent with current knowledge. Furthermore, the simple and highly interpretable logistic-tree has diagnostic performance that is competitive with other complex models.

show abstract

Section: Methods 21 Univariate Evaluation Criterionmentioning

confidence: 99%