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Rapid identification of human ovarian cancer in second harmonic generation images using radiomics feature analyses and tree‐based pipeline optimization tool
Author(s) -
Wang Guangxing,
Sun Yang,
Chen Youting,
Gao Qiqi,
Peng Dongqing,
Lin Hongxin,
Zhan Zhenlin,
Liu Zhiyi,
Zhuo Shuangmu
Publication year - 2020
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.202000050
Subject(s) - pipeline (software) , ovarian cancer , receiver operating characteristic , tree (set theory) , artificial intelligence , computer science , feature (linguistics) , identification (biology) , pattern recognition (psychology) , feature extraction , cancer , stage (stratigraphy) , medicine , machine learning , biology , mathematics , mathematical analysis , linguistics , philosophy , botany , programming language , paleontology
Ovarian cancer is currently one of the most common cancers of the female reproductive organs, and its mortality rate is the highest among all types of gynecologic cancers. Rapid and accurate classification of ovarian cancer plays an important role in the determination of treatment plans and prognoses. Nevertheless, the most commonly used classification method is based on histopathological specimen examination, which is time‐consuming and labor‐intensive. Thus, in this study, we utilize radiomics feature extraction methods and the automated machine learning tree‐based pipeline optimization tool (TOPT) for analysis of 3D, second harmonic generation images of benign, malignant and normal human ovarian tissues, to develop a high‐efficiency computer‐aided diagnostic model. Area under the receiver operating characteristic curve values of 0.98, 0.96 and 0.94 were obtained, respectively, for the classification of the three tissue types. Furthermore, this approach can be readily applied to other related tissues and diseases, and has great potential for improving the efficiency of medical diagnostic processes.