Evidence map›Paper›PMID 35267091›Full record

ArticleEuropean radiology2022

Contrast-free MRI quantitative parameters for early prediction of pathological response to neoadjuvant chemotherapy in breast cancer.

Siyao Du, Si Gao, Ruimeng Zhao, Hongbo Liu, Yan Wang, Xixun Qi, Shu Li, Jibin Cao, Lina Zhang

Abstract read
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In one paragraph

Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–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

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Clinical MR Biomarkers.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026
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  10. Quantitative MR Imaging of the Breast.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
    Review
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  19. Article
  20. Imaging in translational cancer research.Cancer biology & medicine · 2022
    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

9 authors.

Siyao DuDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Si GaoDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Ruimeng ZhaoDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Hongbo LiuDepartment of Epidemiology and Medical Statistics, School of Public Health, China Medical University, No. 77, Puhe Road, Shenyang North Xin Area, Shenyang City, 110122, Liaoning Province, China.
Yan WangDepartment of Pathology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Xixun QiDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Shu LiDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Jibin CaoDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China.
Lina ZhangDepartment of Radiology, The First Affiliated Hospital of China Medical University, No. 155, Nanjing Street, Heping District, Shenyang City, 110001, Liaoning Province, China. zhanglnda@163.com.ORCID http://orcid.org/0000-0002-4079-9692

Funding

National Financial Appropriation Research Project 2017YFC1309100National Scientific Foundation of China 81971695Set Sail Project of the First Affiliated Hospital of China Medical University 36028
6 · The paper itself

Abstract

objectivesTo assess early changes in synthetic relaxometry after neoadjuvant chemotherapy (NAC) for breast cancer and establish a model with contrast-free quantitative parameters for early prediction of pathological response.

methodsFrom March 2019 to January 2021, breast MRI were performed for a primary cohort of women with breast cancer before (n = 102) and after the first (n = 93) and second (n = 90) cycle of NAC. Tumor size, synthetic relaxometry (T1/T2 relaxation time [T1/T2], proton density), and ADC were obtained, and the changes after treatment were calculated. Prediction models were established by multivariate logistic regression; evaluated with discrimination, calibration, and clinical application; and compared with Delong tests, net reclassification (NRI), and integrated discrimination index (IDI). External validation was performed from February to June 2021 with an independent cohort of 35 patients.

resultsIn the primary cohort, all parameters changed after early treatment. Synthetic relaxometry decreased to a greater degree in major histologic responders (MHR, Miller-Payne G4-5) compared with non-MHR (Miller-Payne G1-3). A model combining ADC after treatment, changes in T1 and tumor size, and cancer subtype achieved the highest AUC after the first (primary/validation cohort, 0.83/0.82) and second cycles (primary/validation cohort, 0.85/0.84). No difference of AUC (p ≥ 0.27), NRI (p ≥ 0.31), and IDI (p ≥ 0.32) was found between models with different cycles and size-measured sequences. Model calibration and decision curves demonstrated a good fitness and clinical benefit, respectively.

conclusionsEarly reduction in synthetic relaxometry indicated pathological response to NAC. Contrast-free T1 and ADC combined with size and cancer subtype predicted effectively pathological response after one NAC cycle. KEY POINTS: • Synthetic MRI relaxometry changed after early neoadjuvant chemotherapy, which demonstrated pathological response for mass-like breast cancers. • Contrast-free quantitative parameters including T1 relaxation time and apparent diffusion coefficient, combined with tumor size and cancer subtype, stratified major histologic responders. • A contrast-free model predicted an early pathological response after the first treatment cycle of neoadjuvant chemotherapy.

Indexed as

Breast NeoplasmsNeoadjuvant TherapyBreastFemaleHumansMagnetic Resonance ImagingTreatment OutcomeBreast neoplasmsLogistic modelsMagnetic resonance imagingNeoadjuvant therapy

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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.