Evidence map›Paper›PMID 39976736›Full record

ArticleJournal of cancer research and clinical oncology2025

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.

Shilin Xu, Chengguang Zhu, Meixuan Wu, Sijia Gu, Yongsong Wu, Shanshan Cheng, Chao Wang, Yue Zhang, Weixia Zhang, Wei Shen and 3 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cancer research and clinical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Shilin Xu *Department of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
Chengguang Zhu *MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China.
Meixuan WuDepartment of Obstetrics and Gynecology, School of Medicine, Renji Hospital, Shanghai Jiaotong University, Shanghai, China.
Sijia GuDepartment of Obstetrics and Gynecology, School of Medicine, Renji Hospital, Shanghai Jiaotong University, Shanghai, China.
Yongsong WuDepartment of Obstetrics and Gynecology, School of Medicine, Renji Hospital, Shanghai Jiaotong University, Shanghai, China.
Shanshan ChengDepartment of Obstetrics and Gynecology, School of Medicine, Renji Hospital, Shanghai Jiaotong University, Shanghai, China.
Chao WangDepartment of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
Yue ZhangDepartment of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
Weixia ZhangMoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China.
Wei ShenMoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China. wei.shen@sjtu.edu.cn.
Jiani YangDepartment of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China. sjtuyangjiani@163.com.
Xiaokang YangMoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China.
Yu WangDepartment of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China. renjiwangyu@126.com.

Funding

National Natural Science Foundation of China 82102856Science and Technology Commission of Shanghai Municipality 23YF1433600Shanghai Municipal Science and Technology Major Project 2021SHZDZX0102the Fundamental Research Funds for the Central Universities, the National Key R&D Program of China 2022YFF1202600
6 · The paper itself

Abstract

purposeThe 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.

methodsWe 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).

results183 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.

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

Indexed as

AlgorithmsArtificial IntelligenceFluorodeoxyglucose F18Ovarian NeoplasmsPositron Emission Tomography Computed TomographyAdultAgedFemaleHumansMachine LearningMiddle AgedNeoplasm StagingRadiomicsRadiopharmaceuticalsRetrospective StudiesFluorodeoxyglucose F18Radiopharmaceuticals18F-FDG PET/CTFIGO stageOvarian cancerRadiomics

Identifiers

PMID39976736
PMCPMC11842498

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.