Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features.

IF 2.7 3区 医学 Q3 ONCOLOGY
Shilin Xu, Chengguang Zhu, Meixuan Wu, Sijia Gu, Yongsong Wu, Shanshan Cheng, Chao Wang, Yue Zhang, Weixia Zhang, Wei Shen, Jiani Yang, Xiaokang Yang, Yu Wang
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引用次数: 0

Abstract

Purpose: The International Federation of Gynecology and Obstetric (FIGO) stage is critical to guiding the treatments of ovarian cancer (OC). We tried to develop a model to predict the FIGO stage of OC through machine learning algorithms with patients' pretreatment clinical, positron emission tomography scan (PET/CT) metabolic, and radiomics features.

Methods: We enrolled OC patients who underwent PET/CT scans and divided them into two cohorts according to their FIGO stage. Then we manually delineated the volume of interest (VOI) and calculated PET metabolic features. Other PET/CT radiomics features were extracted by Python. We developed 11 prediction models to predict stages based on four groups of features and conducted three experiments to verify the meaning of PET/CT features. We also redesigned experiments to demonstrate the stage prediction performance in ovarian clear cell carcinoma (OCCC) and mucinous ovarian cancer (MCOC).

Results: 183 OC patients were enrolled in this study, and we obtained 137 features from four groups of data. The best model was an adaptive ensemble with an area under the curve (AUC) value of 0.819. Our proposed models presented the best result of 0.808 in terms of AUC in OCCC and MCOC patients' groups.

Conclusion: Through artificial intelligence (AI) algorithms, the PET/CT metabolic and radiomics features combined with clinical features could improve the accuracy of staging prediction.

结合18F-FDG PET/CT代谢及放射组学特征的临床特征预测卵巢癌FIGO分期的人工智能算法
目的:国际妇产科联合会(FIGO)分期对指导卵巢癌(OC)的治疗至关重要。我们试图通过机器学习算法,结合患者的临床预处理、正电子发射断层扫描(PET/CT)代谢和放射组学特征,建立一个预测OC FIGO分期的模型。方法:我们招募了接受PET/CT扫描的OC患者,并根据他们的FIGO分期将他们分为两组。然后我们手动划定感兴趣体积(VOI)并计算PET代谢特征。其他PET/CT放射组学特征由Python提取。我们基于四组特征开发了11个预测模型来预测阶段,并进行了3次实验来验证PET/CT特征的意义。我们还重新设计了实验,以证明卵巢透明细胞癌(OCCC)和粘液性卵巢癌(MCOC)的分期预测性能。结果:本研究纳入183例OC患者,我们从四组数据中获得137个特征。最佳模型为曲线下面积(AUC)为0.819的自适应综。我们所提出的模型在OCCC和MCOC患者组中AUC的最佳结果为0.808。结论:通过人工智能(AI)算法,PET/CT代谢和放射组学特征结合临床特征可提高分期预测的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.00
自引率
2.80%
发文量
577
审稿时长
2 months
期刊介绍: The "Journal of Cancer Research and Clinical Oncology" publishes significant and up-to-date articles within the fields of experimental and clinical oncology. The journal, which is chiefly devoted to Original papers, also includes Reviews as well as Editorials and Guest editorials on current, controversial topics. The section Letters to the editors provides a forum for a rapid exchange of comments and information concerning previously published papers and topics of current interest. Meeting reports provide current information on the latest results presented at important congresses. The following fields are covered: carcinogenesis - etiology, mechanisms; molecular biology; recent developments in tumor therapy; general diagnosis; laboratory diagnosis; diagnostic and experimental pathology; oncologic surgery; and epidemiology.
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