Evidence map›Paper›PMID 41140909›Full record

ArticleOphthalmology science2026

Swept-Source OCT Angiography Features of Melanotic Choroidal Tumors: An Analysis of 102 Consecutive Cases.

Ruiheng Zhang, Haotian Wu, Yitong Li, Wenda Zhou, Xuhan Shi, Chuyao Yu, Yuhang Yang, Hanqing Zhao, Heyan Li, Shanshan Wang and 9 more

Abstract read
In one paragraph

Article in Ophthalmology science, 2026. 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

19 authors.

Ruiheng ZhangBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Haotian WuBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Yitong LiBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Wenda ZhouBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Xuhan ShiBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Chuyao YuBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Yuhang YangBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Hanqing ZhaoBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Heyan LiBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Shanshan WangBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Jiaoyue DongBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Li DongBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Lei ShaoBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Yaling LiuShenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
Xinyu ZhaoShenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
Zhen YuShenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
Guomin ZhangShenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
Yueming LiuBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Wenbin WeiBeijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To quantitatively analyze swept-source OCT angiography (SS-OCTA) features of choroidal melanoma and nevi, focusing on their distinguishing angiographic features. Design: Case-series study. Participants: This retrospective study included 102 consecutive patients with choroidal melanoma or nevi who were referred to Beijing Tongren Hospital between July 2019 and June 2024. Main Outcome Measures: The choriocapillaris and choroid slabs were en face reconstructed. A 5-grade system was developed based on the density and morphology of disorganized vasculature in the choroid slab. Choriocapillaris flow deficit (CCFD%) void was calculated within circular regions defined by the tumor's radius and diameter. Methods: This retrospective study included 102 consecutive patients with choroidal melanoma or nevi referred to Beijing Tongren Hospital between July 2019 and June 2024. The choriocapillaris and choroid slabs were en face reconstructed. A 5-grade system was developed based on the density and morphology of disorganized vasculature in the choroid slab. Choriocapillaris flow deficit (CCFD%) void was calculated within circular regions defined by the tumor's radius and diameter. Results: Of all the 102 patients, 87 (85%) patients with qualified SS-OCTA images, comprising 61 cases of choroidal melanoma and 26 cases of nevi. Sixty melanoma cases (98%) exhibited disorganized tumor vasculature forming twisted vessel loops and networks on the choroid slab (grade ≥2). Nevi showed 2 types of intrinsic tumor vasculature patterns in the choroidal slab: well-organized (grade 0 or 1) and melanoma-like disorganized networks (grade ≥2). Classified based on multimodal imaging risk score, nevi with a high risk for growth (≥2 risk factors) were more likely to exhibit melanoma-like disorganized networks tumor vasculature (82% vs. 27%, Conclusions: Swept-source OCT angiography enables detailed noninvasive assessment of intrinsic vasculature in melanotic choroidal tumors. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Indexed as

Choroidal melanomaChoroidalneviOCT angiography

Identifiers

PMID41140909
PMCPMC12548087

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.