Evidence map›Paper›PMID 42622829›Full record

ArticleAbdominal radiology (New York)2026

Intravoxel incoherent motion imaging with improved image quality by deep learning reconstruction for prediction of lymph-vascular space invasion in patients with cervical cancer.

Ying Zhao, Nan Wang, Changjun Ma, Shifeng Tian, Jun Li, Liangjie Lin, Qingwei Song, Ailian Liu

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Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ying ZhaoFirst Affiliated Hospital of Dalian Medical University, Dalian, China.
Nan WangFirst Affiliated Hospital of Dalian Medical University, Dalian, China.
Changjun MaFirst Affiliated Hospital of Dalian Medical University, Dalian, China.
Shifeng TianFirst Affiliated Hospital of Dalian Medical University, Dalian, China.
Jun LiFirst Affiliated Hospital of Dalian Medical University, Dalian, China.
Liangjie LinPhilips (China), Beijing, China.
Qingwei SongFirst Affiliated Hospital of Dalian Medical University, Dalian, China.
Ailian LiuFirst Affiliated Hospital of Dalian Medical University, Dalian, China. liuailian@dmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the performance of intravoxel incoherent motion (IVIM) imaging with deep learning reconstruction (DLR) for prediction of lymph-vascular space invasion (LVSI) in cervical cancer (CC).

methodsPatients clinically suspected of having CC who underwent uterine MRI were retrospectively collected. IVIM images were collected with b values of 0, 20, 50, 100, 150, 200, 400, 800, 1500 and 2000 s/mm

resultsThe study included 55 patients with 31/55 (56.36%) lesions identified as LVSI positive. IVIM images with DLR were associated with significantly higher image quality scores, SNR, and CNR than those without DLR. All quantitative parameters except ADC

conclusionReconstruction of pelvic IVIM images with DLR significantly improved image quality. IVIM parameters derived from DLR images showed potential for non-invasive prediction of LVSI status in CC.

Indexed as

Cervical cancerDeep learningDiffusion-weighted imagingIntravoxel incoherent motionLymph-vascular space invasionMagnetic resonance imaging

Identifiers

PMID42622829

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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.