Application of Nonlinear Models Combined with Conventional Laboratory Indicators for the Diagnosis and Differential Diagnosis of Ovarian Cancer

Existing biomarkers for ovarian cancer lack sensitivity and specificity. We compared the diagnostic efficacy of nonlinear machine learning and linear statistical models for diagnosing ovarian cancer using a combination of conventional laboratory indicators. We divided 901 retrospective samples into...

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Bibliographic Details
Published in:Journal of clinical medicine 2023-01, Vol.12 (3), p.844
Main Authors: Zhang, Tongshuo, Pang, Aibo, Lyu, Jungang, Ren, Hefei, Song, Jiangnan, Zhu, Feng, Liu, Jinlong, Cui, Yuntao, Ling, Cunbao, Tian, Yaping
Format: Article
Language:English
Subjects:
Antigens
Artificial intelligence
Biomarkers
Blood
Cervical cancer
Cholesterol
Clinical medicine
Cysts
Endometrial cancer
Fallopian tubes
Generalized linear models
Gynecological diseases
Gynecology
High density lipoprotein
Hospitals
Inflammatory diseases
Laboratories
Lymphocytes
Machine learning
Neutrophils
Ovarian cancer
Pelvic inflammatory disease
Standard scores
Tumors
Ultrasonic imaging
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Summary:Existing biomarkers for ovarian cancer lack sensitivity and specificity. We compared the diagnostic efficacy of nonlinear machine learning and linear statistical models for diagnosing ovarian cancer using a combination of conventional laboratory indicators. We divided 901 retrospective samples into an ovarian cancer group and a control group, comprising non-ovarian malignant gynecological tumor (NOMGT), benign gynecological disease (BGD), and healthy control subgroups. Cases were randomly assigned to training and internal validation sets. Two linear (logistic regression (LR) and Fisher's linear discriminant (FLD)) and three nonlinear models (support vector machine (SVM), random forest (RF), and artificial neural network (ANN)) were constructed using 22 conventional laboratory indicators and three demographic characteristics. Model performance was compared. In an independent prospectively recruited validation set, the order of diagnostic efficiency was RF, SVM, ANN, FLD, LR, and carbohydrate antigen 125 (CA125)-only (AUC, accuracy: 0.989, 95.6%; 0.985, 94.4%; 0.974, 93.4%; 0.915, 82.1%; 0.859, 80.1%; and 0.732, 73.0%, respectively). RF maintained satisfactory classification performance for identifying different ovarian cancer stages and for discriminating it from NOMGT-, BGD-, or CA125-positive control. Nonlinear models outperformed linear models, indicating that nonlinear machine learning models can efficiently use conventional laboratory indicators for ovarian cancer diagnosis.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm12030844