Evidence map›Paper›PMID 34688263›Full record

ArticleBMC medical imaging2021

Predicting pathologic response to neoadjuvant chemotherapy in patients with locally advanced breast cancer using multiparametric MRI.

Nannan Lu, Jie Dong, Xin Fang, Lufang Wang, Wei Jia, Qiong Zhou, Lingyu Wang, Jie Wei, Yueyin Pan, Xinghua Han

Open access · goldAbstract read
In one paragraph

Article in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
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

7 citing papers in PubMed, 9 citations in OpenAlex.

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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

10 authors at 2 institutions in 1 country.

Nannan LuDepartment of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.
Jie DongDepartment of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.
Xin FangDepartment of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230031, Anhui, China.
Lufang WangDepartment of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.
Wei JiaDepartment of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.
Qiong ZhouDepartment of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.
Lingyu WangDepartment of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.
Jie WeiDepartment of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.
Yueyin PanDepartment of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.
Xinghua HanDepartment of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China. ahslhxh@163.com.
University of Science and Technology of China · CNAnhui Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aims to observe and analyze the effect of diffusion weighted magnetic resonance imaging (MRI) on the patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy.

methodsFifty patients (mean age, 48.7 years) with stage II-III breast cancer who underwent neoadjuvant chemotherapy and preoperative MRI between 2016 and 2020 were retrospectively evaluated. The associations between preoperative breast MRI findings/clinicopathological features and outcomes of neoadjuvant chemotherapy were assessed.

resultsClinical stage at baseline (OR: 0.104, 95% confidence interval (CI) 0.021-0.516, P = 0.006) and standard apparent diffusion coefficient (ADC) change (OR: 9.865, 95% CI 1.024-95.021, P = 0.048) were significant predictive factors of the effects of neoadjuvant chemotherapy. The percentage increase of standard ADC value in pathologic complete response (pCR) group was larger than that in non-pCR group at first time point (P < 0.05). A correlation was observed between the change in standard ADC values and tumor diameter at first follow-up (r: 0.438, P < 0.05).

conclusionsOur findings support that change in standard ADC values and clinical stage at baseline can predict the effects of neoadjuvant chemotherapy for patients with breast cancer in early stage.

Indexed as

Multiparametric Magnetic Resonance ImagingBreast NeoplasmsChemotherapy, AdjuvantFemaleHumansMiddle AgedNeoadjuvant TherapyNeoplasm StagingPredictive Value of TestsRetrospective StudiesBreast cancerNeoadjuvant chemotherapyPathologic complete responseStandard apparent diffusion coefficient

Identifiers

PMID34688263
PMCPMC8542288
OpenAlexW3208248710

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Registered trials

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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.