Evidence map›Paper›PMID 41390830›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2025

A dual-core driven hybrid radiomics integrates multiple views of greyscale ultrasound image for the early management from diagnosis to biopsy suggestion of BI-RADS 4 breast lesions: a prospective multicenter study.

Bo-Yang Zhou, Li-Ping Sun, Han-Sheng Xia, Bin Tan, Yi-Lei Shi, Hui Liu, Chuan Qin, Yi-Kang Sun, Li-Fan Wang, Xin Guan and 7 more

Abstract readMulticenter Study
In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

17 authors.

Bo-Yang Zhou *Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Li-Ping Sun *Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Shanghai, China.
Han-Sheng Xia *Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Bin Tan *MedAI Technology (Wuxi) Co., Ltd., Wuxi, China.
Yi-Lei Shi *MedAI Technology (Wuxi) Co., Ltd., Wuxi, China.
Hui LiuDepartment of Medical Ultrasound, Shanghai Tenth People's Hospital, Shanghai, China.
Chuan QinDepartment of Ultrasound, Jinshan Hospital, Fudan University, Shanghai, China.
Yi-Kang SunDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Li-Fan WangDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Xin GuanDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Dan LuDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Xin YeDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Hong HanDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China.
Bin HuangDepartment of Ultrasound, Zhejiang Hospital, Hangzhou, Zhejiang Province, China. hb2k@163.com.
Xiao-Xiang ZhuData Science in Earth Observation, Technical University of Munich, Munich, Germany. xiaoxiang.zhu@tum.de.
Chong-Ke ZhaoDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China. zhaochongke123@163.com.
Hui-Xiong XuDepartment of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, China. xuhuixiong@126.com.

Funding

Fundamental Research Funds for the Central Universities 22120190213 and ZD-11-202151Horizontal Project of Zhongshan Hospital of Fudan University H2024-077Ministry of Science and Technology of China 2023YFC2414204National Natural Science Foundation of China 82202174Pilot Medical Construction Project of Fudan University IDF 152076Science and Technology Commission of Shanghai Municipality 18441905500 and 19DZ2251100Scientific Research and Development Fund of Zhongshan Hospital of Fudan University 2022ZSQD07 and ZSLCYJ202317Shanghai Municipal Health Commission SHSLCZDZK03502Shanghai Science and Technology Innovation Action Plan 21Y11911200 and 21Y11910800
6 · The paper itself

Abstract

objectivesThis study aimed to investigate the potential of ultrasound (US) radiomics approaches based on machine learning (ML) and deep learning (DL) in the early management from diagnosis to biopsy suggestion of Breast Imaging Reporting and Data System (BI-RADS) 4 breast lesions.

methodsFrom January 2020 to December 2022, this multicenter study prospectively evaluated a dataset of dual-view greyscale US images in 1054 patients with 1062 BI-RADS 4 lesions from three centers. Three types of radiomic approaches were developed and compared: (i) the ML-based radiomics; (ii) the DL-based radiomics; and (iii) the dual-core driven hybrid radiomics (Dc-HR) approach. The performance and unnecessary biopsy rate of each model and shear wave elastography (SWE) or contrast-enhanced ultrasound (CEUS) assessment was assessed and compared.

resultsThe diagnostic performance of Dc-HR (area under the curve [AUC]: 0.944–0.980) outperformed DL-based radiomics (AUC: 0.839–0.964) and ML-based radiomics (AUC: 0.544–0.882). The Dc-HR achieved a better performance in identifying malignancy than the assessment with SWE (AUCs: 0.584–0.725) and CEUS (AUCs: 0.813–0.840). The Dc-HR approach could reduce 90.70–100% unnecessary biopsies in BI-RADS 4a lesions without extra SWE or CEUS examinations, while maintaining a high positive predictive value of 96.43–100% in BI-RADS 4b and 4c lesions. The Dc-HR approach applied with dual-view greyscale US images yielded the highest net benefit according to the Decision curve analyses in comparison to other approaches.

conclusionThe Dc-HR approach might be a promising method in diagnosing BI-RADS 4 breast lesions and reducing unnecessary biopsy rate significantly, especially in areas where extra SWE or CEUS examinations are not accessible easily.

Indexed as

Breast NeoplasmsRadiomicsUltrasonography, MammaryAdultAgedBiopsyDeep LearningFemaleHumansMachine LearningMiddle AgedProspective StudiesBreast cancerDeep learningDual-view ultrasound imagesMachine learningUnnecessary biopsy rate

Identifiers

PMID41390830
PMCPMC12822342

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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.