Evidence map›Paper›PMID 35406422›Full record

ArticleCancers2022

A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker.

Ching-Wei Wang, Yu-Ching Lee, Cheng-Chang Chang, Yi-Jia Lin, Yi-An Liou, Po-Chao Hsu, Chun-Chieh Chang, Aung-Kyaw-Oo Sai, Chih-Hung Wang, Tai-Kuang Chao

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In one paragraph

Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

23 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ching-Wei WangGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.ORCID 0000-0001-9992-6863
Yu-Ching LeeGraduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Cheng-Chang ChangDepartment of Gynecology and Obstetrics, Tri-Service General Hospital, Taipei 11490, Taiwan.ORCID 0000-0002-7356-446X
Yi-Jia LinDepartment of Pathology, Tri-Service General Hospital, Taipei 11490, Taiwan.ORCID 0000-0002-8073-8659
Yi-An LiouGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Po-Chao HsuDepartment of Gynecology and Obstetrics, Tri-Service General Hospital, Taipei 11490, Taiwan.ORCID 0000-0002-2772-7803
Chun-Chieh ChangGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Aung-Kyaw-Oo SaiGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan.
Chih-Hung WangDepartment of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, Taipei 11490, Taiwan.ORCID 0000-0001-5058-4356
Tai-Kuang ChaoDepartment of Pathology, Tri-Service General Hospital, Taipei 11490, Taiwan.ORCID 0000-0001-8219-6382

Funding

Ministry of Science and Technology, Taiwan MOST109-2221-E-011-018-MY3, MOST-110-2321-B-016-002National Taiwan University of Science and Technology, Taiwan TSGH-NTUST-111-05Tri-Service General Hospital, Taiwan TSGHC108086, TSGH-D-109094, TSGH-D-110036 and TSGH-A-111010
6 · The paper itself

Abstract

Ovarian cancer is a common malignant gynecological disease. Molecular target therapy, i.e., antiangiogenesis with bevacizumab, was found to be effective in some patients of epithelial ovarian cancer (EOC). Although careful patient selection is essential, there are currently no biomarkers available for routine therapeutic usage. To the authors’ best knowledge, this is the first automated precision oncology framework to effectively identify and select EOC and peritoneal serous papillary carcinoma (PSPC) patients with positive therapeutic effect. From March 2013 to January 2021, we have a database, containing four kinds of immunohistochemical tissue samples, including AIM2, c3, C5 and NLRP3, from patients diagnosed with EOC and PSPC and treated with bevacizumab in a hospital-based retrospective study. We developed a hybrid deep learning framework and weakly supervised deep learning models for each potential biomarker, and the experimental results show that the proposed model in combination with AIM2 achieves high accuracy 0.92, recall 0.97, F-measure 0.93 and AUC 0.97 for the first experiment (66% training and 34%testing) and high accuracy 0.86 ± 0.07, precision 0.9 ± 0.07, recall 0.85 ± 0.06, F-measure 0.87 ± 0.06 and AUC 0.91 ± 0.05 for the second experiment using five-fold cross validation, respectively. Both Kaplan-Meier PFS analysis and Cox proportional hazards model analysis further confirmed that the proposed AIM2-DL model is able to distinguish patients gaining positive therapeutic effects with low cancer recurrence from patients with disease progression after treatment (p < 0.005).

Indexed as

deep learningovarian cancerprecision oncologyweakly supervised learning

Identifiers

PMID35406422
PMCPMC8996991

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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.