Evidence map›Paper›PMID 42023157›Full record

ArticleiScience2026

SCAR-Net-assisted ultrasound diagnosis of postoperative scars and recurrent lesions in breast cancer.

Na Feng, Shanshan Zhao, Zhikai Lei, Dan Yi, Jincao Yao, Xiao Zhang, Xiangyang Li, Weijie Zou, Jian Zhang, Liyu Chen and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Na FengDepartment of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
Shanshan ZhaoDepartment of Ultrasound, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing, China.
Zhikai LeiThe First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
Dan YiDepartment of Ultrasound, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing, China.
Jincao YaoDepartment of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
Xiao ZhangThe First People's Hospital of Lin'an District, Hangzhou, China.
Xiangyang LiWenzhou Medical University, Wenzhou, China.
Weijie ZouDepartment of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
Jian ZhangThe First People's Hospital of Lin'an District, Hangzhou, China.
Liyu ChenDepartment of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
Chen YangDepartment of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
Dong XuDepartment of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
Yong WuDepartment of Medical Engineering, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Postoperative differentiation between scar tissue and recurrent lesions in patients with breast cancer presents a significant diagnostic challenge. This study introduces SCAR-Net, a deep learning model specifically designed for ultrasound-based discrimination between these similar-appearing tissues. Using 34,376 ultrasound images from 5,710 patients across four hospitals, we developed a model incorporating scar-recurrence feature enhancer and boundary-sensitive attention network modules. In multicenter validation, SCAR-Net significantly improved radiologists' diagnostic performance, increasing AUC from 0.810-0.824 to 0.939-0.942, sensitivity from 0.775-0.782 to 0.934-0.941, and specificity from 0.839-0.872 to 0.935-0.950 (all

Indexed as

artificial intelligence applicationsmedical imagingoncology

Identifiers

PMID42023157
PMCPMC13098497

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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.