Evidence map›Paper›PMID 41972087›Full record

ArticleQuantitative imaging in medicine and surgery2026

Optimizing abbreviated breast MRI protocols for the early assessment of neoadjuvant chemotherapy response in breast cancer.

Yongxin Chen, Siyi Chen, Ying Li, Xueli Li, Yihong Lin, Yi Sui, Xiaomeng Yu, Wenke Hu, Qingcong Kong, Zhou Liu and 3 more

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 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

13 authors.

Yongxin Chen *Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Siyi Chen *Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Ying Li *Department of Radiology, The Third People's Hospital of Honghe Hani and Yi Autonomous Prefecture, Honghe, China.
Xueli LiDepartment of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Yihong LinInformation Department, Southern Medical University Shunde Hospital (Shunde District People's Hospital of Foshan City), Foshan, China.
Yi SuiDepartment of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Xiaomeng YuDepartment of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Wenke HuDepartment of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Qingcong KongDepartment of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Zhou Liu *Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
Wenjie Tang *Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Xinqing Jiang *Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.
Yuan Guo *Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The widespread use of breast magnetic resonance imaging (MRI) for monitoring tumor response to neoadjuvant chemotherapy (NAC) is restricted by its limited accessibility and substantial interpretive workload. This study aimed to evaluate various abbreviated breast MRI protocols and identify the optimal approach for the early assessment of NAC response in breast cancer patients. Methods: A total of 359 patients with invasive breast cancer from three centers, who underwent full-protocol breast MRI at baseline and after two cycles of NAC, were retrospectively included in the study [primary cohort, n=169; external validation cohorts (EVCs), n=89 and 101]. Six abbreviated protocols were reconstructed from the full protocol, each comprising T2-weighted imaging plus a single dynamic contrast-enhanced (DCE) phase acquired at approximately 20, 90, or 270 s after injection; diffusion-weighted imaging (DWI) was also included in Protocols 4-6. Percentage changes in tumor size, the tumor-to-parenchyma signal enhancement ratio (SER), and the apparent diffusion coefficient (ADC) between MRI at the baseline and after two cycles of NAC (Δ%Size, Δ%SER, and Δ%ADC) were calculated. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis with the area under the curve (AUC), and compared using the DeLong test. Scan acquisition and interpretation times were also recorded to assess efficiency. Results: Δ%Size, Δ%SER, and Δ%ADC were associated with pathologic complete response (all P<0.05). The AUCs ranged from 0.734 to 0.876 in the primary cohort and from 0.759 to 0.878 and 0.771 to 0.880 in the two EVCs. Under the same enhancement duration, adding DWI significantly improved the AUC value in the primary cohort (P1 Conclusions: The abbreviated MRI protocols showed potential for the early assessment of NAC response in breast cancer and shortened the acquisition time. Prolonged enhancement phases did not yield diagnostic gains, while the inclusion of DWI improved diagnostic performance but lengthened the interpretation time. Integrating DWI with shorter enhancement may offer a balanced approach between accuracy and efficiency.

Indexed as

Abbreviated protocolbreast cancermagnetic resonance imaging (MRI)neoadjuvant chemotherapy (NAC)treatment response

Identifiers

PMID41972087
PMCPMC13066854

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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.