Evidence map›Paper›PMID 41586963›Full record

ArticleInsights into imaging2026

Multiregional MRI-based deep learning radiomics to predict axillary response after neoadjuvant chemotherapy in breast cancer patients.

Weiyue Chen, Guihan Lin, Yi Zhou, Yongjun Chen, Changsheng Shi, Ting Zhao, Zhihan Yan, Zhiyi Peng, Shuiwei Xia, Min Xu and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 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.

Weiyue Chen *Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
Guihan Lin *Zhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
Yi ZhouZhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
Yongjun ChenDepartment of Radiology, The Sixth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
Changsheng ShiDepartment of Interventional Vascular Surgery, The Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Ting ZhaoZhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
Zhihan YanWenzhou Key Laboratory of Structural and Functional Imaging, Wenzhou, China.
Zhiyi PengDepartment of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Shuiwei XiaZhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
Min XuZhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
Minjiang ChenZhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
Chenying LuZhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
Jiansong JiZhejiang Key Laboratory of Imaging and Interventional Medicine, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China. jjstcty@wmu.edu.cn.ORCID http://orcid.org/0000-0002-6026-3676

Funding

Key Project of Joint Construction by Provincial and Ministerial Authorities WKJ-ZJ-2452Public Welfare Technology Application Research Project of Lishui City 2024GYX45
6 · The paper itself

Abstract

objectivesThis study was designed to develop a multiregional MRI-based deep learning radiomics nomogram (DLRN) for predicting axillary pathological complete response (apCR) after neoadjuvant chemotherapy (NAC) in breast cancer. MATERIALS AND

methodsIn total, 539 patients in our hospital were randomly split into a training cohort (TC; n = 431) and an internal validation cohort (IVC; n = 108), and 703 patients were recruited from three external centers as external validation cohorts (EVC1-3). Uni- and multivariate analyses were performed to select clinicopathological characteristics and establish a clinical model. DLR models were constructed based on DL and handcrafted radiomics features extracted from gross tumor volume (GTV) and GTV incorporating 3-, 5-, 7-, and 9-mm peritumoral regions (GPTV

resultsThe GPTV

conclusionThe DLRN model, based on the GPTV CRITICAL RELEVANCE STATEMENT: The deep learning radiomics nomogram based on intra- and peritumoral regions could noninvasively predict axillary pCR in breast cancer patients receiving NAC, which might prevent patients from undergoing unnecessary axillary lymph node dissection. KEY POINTS: Combining intratumoral and 5-mm peritumoral region radiomics had the highest predictive efficiency for axillary pCR after NAC in breast cancer. The deep learning radiomics nomogram based on intra- and peritumoral regions outperformed the clinical model. The proposed model could provide a noninvasive and easy-to-use tool to offer decision support for optimizing treatments.

Indexed as

Axillary lymph nodeBreast cancerDeep learning radiomicsNeoadjuvant chemotherapyPeritumoral

Identifiers

PMID41586963
PMCPMC12835486

What OpenQuestion holds

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LicenceCC BY
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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.