Evidence map›Paper›PMID 41522058›Full record

ArticleQuantitative imaging in medicine and surgery2026

AI-assisted compressed sensing and gadoxetic acid-enhanced MRI for evaluating colorectal liver metastases in complex hepatic backgrounds: a prospective 5T MRI study.

Xiaoer Zhao, Ying Liu, Shaopeng Li, Peng Wang, Hao Chen, Dawei Yin, Xingwang Wu

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

7 authors.

Xiaoer ZhaoDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0002-0368-4327
Ying LiuDepartment of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.ORCID https://orcid.org/0009-0004-3910-6977
Shaopeng LiDepartment of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.ORCID https://orcid.org/0000-0002-8558-9816
Peng WangDepartment of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.ORCID https://orcid.org/0000-0003-0110-5579
Hao ChenDepartment of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.ORCID https://orcid.org/0000-0002-6650-8240
Dawei YinDepartment of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.ORCID https://orcid.org/0000-0002-7504-3852
Xingwang WuDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.ORCID https://orcid.org/0000-0002-9481-9425

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The detection of colorectal liver metastases (CRLMs) in complex hepatic backgrounds due to chemotherapy-associated liver injury (CALI) and prior local CRLM treatment is challenging and requires advanced imaging modalities capable of providing both precise lesion detection and CALI assessments. This study aimed to evaluate the diagnostic performance of 5 Tesla (T) multimodal magnetic resonance imaging (MRI) for detecting CRLMs while accurately assessing the hepatic background. Methods: A total of 35 post-chemotherapy patients with suspected CRLMs and a combined total of 118 MRI-identified lesions were prospectively enrolled. Participants underwent 5T liver multimodal MRI with acquisition of: T1-weighted (T1W) in/out-of-phase imaging and proton density fat fraction (PDFF), susceptibility-weighted imaging with fast technique (uSWIFT), and standard hepatic gadoxetic acid-enhanced MRI (EOB-MRI) with three-dimensional (3D) isotropic T1W fast spoiled gradient-recalled echo (FSPGR) hepatobiliary phase imaging and artificial intelligence (AI)-assisted compressed sensing (ACS-HBP) (acquisition voxel size 1.2 mm Results: The combination of ACS-HBP and diffusion-weighted imaging (DWI) demonstrated excellent diagnostic performance in detecting CRLMs, yielding the highest sensitivity (97.2%) and PPV (94.6-95.5%), whereas for small lesions (≤10 mm), the combination yielded a sensitivity of 95.7%, outperforming DWI alone (66.0%). Neither CALI nor a prior history of local CRLM treatment had a significant impact on diagnostic performance (all P>0.05). On ACS-HBP imaging, 86.1% (93/108) of all lesions and 71.7% (33/46) of lesions ≤10 mm presented with a target sign (central hyperintensity with a hypointense rim), or reverse target sign (central hypointensity with a hyperintense rim). Conclusions: The combination of ACS and 5T EOB-MRI demonstrates excellent diagnostic performance for CRLMs, including for ≤10 mm lesions, with high sensitivity and PPV across diverse hepatic backgrounds. On HBP imaging, CRLMs characteristically display target and reverse-target signs, especially in lesions ≤10 mm, facilitating differentiation from hepatic cysts.

Indexed as

5 Tesla (5T)artificial intelligence-assisted compressed sensing (ACS)chemotherapy-associated liver injury (CALI)colorectal liver metastases (CRLMs)gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI)

Identifiers

PMID41522058
PMCPMC12780568

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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.