Evidence map›Paper›PMID 41228259›Full record

ArticleCancers2025

OTC-NET: A Multimodal Method for Accurate Diagnosis of Ovarian Cancer in O-RADS Category 4 Masses.

Peizhong Liu, Yidan Ruan, Yuling Fan, Ping Li, Zhuosheng Liu, Shengjie Wu, Xinying Zheng, Xiuming Wu, Yiting Liu, Shunlan Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Peizhong LiuCollege of Engineering, Huaqiao University, Quanzhou 362021, China.ORCID 0000-0002-0809-6364
Yidan RuanCollege of Medicine, Huaqiao University, Quanzhou 362021, China.
Yuling FanCollege of Engineering, Huaqiao University, Quanzhou 362021, China.
Ping LiDepartment of Obstetrics and Gynecology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China.
Zhuosheng LiuCollege of Medicine, Huaqiao University, Quanzhou 362021, China.ORCID 0009-0008-4995-4207
Shengjie WuCollege of Engineering, Huaqiao University, Quanzhou 362021, China.
Xinying ZhengDepartment of Ultrasound Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.
Xiuming WuDepartment of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China.
Yiting LiuCollege of Medicine, Shenzhen University, Shenzhen 518055, China.
Shunlan LiuDepartment of Ultrasound Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China.

Funding

Natural Science Foundation of Fujian Province 2024J08061Scientific Research Funds of Huaqiao University No. 605-50Y23038
6 · The paper itself

Abstract

backgroundOvarian cancer is the deadliest female reproductive malignancy. Accurate preoperative differentiation of benign and malignant ovarian masses is critical for appropriate treatment. O-RADS category 4 lesions present a wide range of malignant risk, challenging radiologists. Ultrasonic images are the primary focus of current deep learning models, with no consideration for clinical data.

methodsWe proposed OTC-NET, a model that uses multimodal data for classification, which combines ultrasound images and clinical information to improve the classification ability of O-RADS 4 ovarian masses.

resultsOTC-NET outperforms seven deep learning models and three radiologists of varying experience, with AUC significantly higher than junior (

conclusionsThese results indicate that OTC-NET provides superior diagnostic accuracy and has strong potential for clinical application.

Indexed as

deep learningO-RADSovarian lesionsradiologistsultrasound

Identifiers

PMID41228259
PMCPMC12606778

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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.