Evidence map›Paper›PMID 39394171›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2024

Multimodal apparent diffusion MRI model in noninvasive evaluation of breast cancer and Ki-67 expression.

Huan Chang, Jinming Chen, Dawei Wang, Hongxia Li, Lei Ming, Yuting Li, Dan Yu, Yu Xin Yang, Peng Kong, Wenjing Jia and 3 more

Abstract read
In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

13 authors.

Huan ChangDepartment of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, No.16766 Jingshi Road, Jinan, Shandong, China.
Jinming ChenDepartment of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, No.16766 Jingshi Road, Jinan, Shandong, China.
Dawei WangDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China.
Hongxia LiDepartment of Radiology, The Second Hospital of Shandong University, Jinan, Shandong, China.
Lei MingDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China.
Yuting LiDepartment of Radiology, The First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Dan YuUnited Imaging Research Institute of Intelligent Imaging, Beijing, People's Republic of China.
Yu Xin YangUnited Imaging Research Institute of Intelligent Imaging, Beijing, People's Republic of China.
Peng KongDepartment of Breast Surgery, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China.
Wenjing JiaDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong First Medical Universityand, Shandong Academy of Medical Sciences , Jinan, Shandong, China.
Qingqing YanDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong First Medical Universityand, Shandong Academy of Medical Sciences , Jinan, Shandong, China.
Xinhui LiuDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong First Medical Universityand, Shandong Academy of Medical Sciences , Jinan, Shandong, China.
Qingshi ZengDepartment of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, No.16766 Jingshi Road, Jinan, Shandong, China. zengqingshi@sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo assess the capability of multimodal apparent diffusion (MAD) weighted magnetic resonance imaging (MRI) to distinguish between malignant and benign breast lesions, and to predict Ki-67 expression level in breast cancer.

methodsThis retrospective study was conducted with 93 patients who had postoperative pathology-confirmed breast cancer or benign breast lesions. MAD images were acquired using a 3.0 T MRI scanner with 16 b values. The MAD parameters, as flow (f

resultsThe f

conclusionsMAD weighted MRI is a useful method for the breast lesions diagnostics and the preoperative prediction of Ki-67 expression level.

Indexed as

Breast NeoplasmsDiffusion Magnetic Resonance ImagingKi-67 AntigenAdultAgedDiagnosis, DifferentialFemaleHumansMiddle AgedMultimodal ImagingRetrospective StudiesROC CurveKi-67 AntigenBreast cancerDiffusion magnetic resonance imagingMultimodal apparent diffusion analysis

Identifiers

PMID39394171
PMCPMC11470582

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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.