Open AccessMachine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images
Author(s) -
Guangxing Wang,
Yang Sun,
Shuisen Jiang,
Guizhu Wu,
Wenliang Liao,
Yuwei Chen,
Lin Zhang,
Zhiyi Liu,
Shuangmu Zhuo
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.429918
Subject(s) - ovarian cancer , artificial intelligence , computer science , malignancy , classifier (uml) , medicine , ovarian carcinoma , computer aided diagnosis , machine learning , receiver operating characteristic , medical imaging , radiology , cancer , pathology
Regarding growth pattern and cytological characteristics, borderline ovarian tumors fall between benign and malignant, but they tend to develop malignancy. Currently, it is difficult to accurately diagnose ovarian cancer using common medical imaging methods, and histopathological examination is routinely used to obtain a definitive diagnosis. However, such examination requires experienced pathologists, being labor-intensive, time-consuming, and possibly leading to interobserver bias. By using second-harmonic generation imaging and k-nearest neighbors classifier in conjunction with automated machine learning tree-based pipeline optimization tool, we developed a computer-aided diagnosis method to classify ovarian tissues as being malignant, benign, borderline, and normal, obtaining areas under the receiver operating characteristic curve of 1.00, 0.99, 0.98, and 0.97, respectively. These results suggest that diagnosis based on second-harmonic generation images and machine learning can support the rapid and accurate detection of ovarian cancer in clinical practice.