Evidence map›Paper›PMID 42129011›Full record

ArticleInsights into imaging2026

Microstructural mapping of time-dependent diffusion MRI for predicting lymphovascular invasion in rectal cancer: a proof-of-concept investigation.

Fulin Lu, Yanwan Li, Ran Wu, Kuide Li, Xiaoli Chen, Yisha Liu, Bin Luo, Meining Chen, Hang Li

Abstract read
In one paragraph

Article in Insights into imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Fulin Lu *Department of Radiology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Yanwan Li *Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Sichuan Cancer Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Ran Wu *Department of Radiology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Kuide LiDepartment of Radiology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Xiaoli ChenDepartment of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Sichuan Cancer Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Yisha LiuDepartment of Pathology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Bin LuoDepartment of Gastrointestinal Surgery, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Meining ChenDepartment of MR Scientific Marketing, Siemens Healthineers, Shanghai, China.
Hang LiDepartment of Radiology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. lihang111222@126.com.ORCID http://orcid.org/0000-0003-2920-0025

Funding

Key Research and Development Program of Sichuan Province No.2022YFS0249the Beijing Medical and Health Public Welfare Foundation No.YWJKJJHKYJJ-KCYX2025001the Excellent Youth Fund of Sichuan Cancer Hospital No.YB2025018
6 · The paper itself

Abstract

objectiveTo evaluate the value of time-dependent diffusion MRI (td-dMRI) derived microstructural parameters for predicting lymphovascular invasion (LVI) in rectal cancer. MATERIALS AND

methodsEighty-four resectable rectal cancer patients (stage T1, T2, T3a, T3b, and T4a) who underwent preoperative td-dMRI between March 2023 and June 2025 without neoadjuvant therapy were enrolled. Manual segmentation of tumors was performed by an experienced radiologist on each tumor's largest cross-sectional area. Microstructural parameters (intracellular volume fraction (ICVF), cell diameter, extracellular diffusivity and cellularity) were fitted using the limited spectrally edited diffusion model implemented in MATLAB (MathWorks, Inc.). Apparent diffusion coefficient (ADC) values at different diffusion times, relative ADC, ADC ratio, and MRI-reported extramural vascular invasion (EMVI) were also evaluated. Mann-Whitney U test was used to evaluate parameter differences between LVI-positive and LVI-negative. Logistic regression and receiver operating characteristic (ROC) curves (with DeLong test) were used to identify predictors of LVI and diagnostic performance.

resultsOf 84 participants (median age, 66 years; IQR, 60-70 years; 50 male), 30 were LVI-positive and 54 LVI-negative. ICVF, cell diameter, and cellularity were significantly higher in LVI-positive cases (all p < 0.05). MRI-EMVI (OR = 3.251), ICVF (OR = 8.137), and cellularity (OR = 1.159) were independent risk factors of LVI. The combined model integrating MRI-reported EMVI, cellularity, and ICVF achieved an area under the ROC curve (AUC) of 0.860, outperforming individual parameters including MRI-reported EMVI (AUC = 0.730), ICVF (AUC = 0.815), cellularity (AUC = 0.792) and ADC measurements (AUC = 0.631-0.710) (all p < 0.05).

conclusiontd-dMRI-derived parameters, especially ICVF and cellularity combined with MRI-reported EMVI, show potential as noninvasive biomarkers for LVI prediction in rectal cancer. CRITICAL RELEVANCE STATEMENT: This study develops a preoperative time-dependent diffusion MRI-based microstructure parameters model that diagnoses and predicts lymphovascular invasion of rectal cancer, improving diagnostic accuracy and advancing personalized treatment strategies in clinical radiology. KEY POINTS: The time-dependent diffusion MRI-derived microstructural parameters model and clinical data for predicting lymphovascular invasion in rectal cancer. The combined model outperforms single-modality models with 0.860 AUC and 96.3% specificity. The combined model provides a noninvasive, reliable tool for personalized lymphovascular invasion diagnosis and treatment planning.

Indexed as

Diffusion MRILymphovascular invasionMicrostructuralPredictive modelRectal cancer

Identifiers

PMID42129011
PMCPMC13172102

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.