Evidence map›Paper›PMID 42351529›Full record

ReviewDiagnostics (Basel, Switzerland)2026

Advanced Diffusion MRI in Cervical Cancer: A Comprehensive Review.

Ali S Alyami

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 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

1 author.

Ali S AlyamiDiagnostic Radiography Technology (DRT) Department, College of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia.ORCID 0000-0001-5774-5466

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advanced diffusion MRI techniques, particularly intravoxel incoherent motion (IVIM) and diffusion tensor imaging (DTI), have emerged as promising functional imaging tools for improving cervical cancer assessment beyond conventional anatomical MRI. This narrative review summarizes current evidence on the clinical utility of these diffusion-based techniques for tumor characterization, local staging, parametrial invasion, lymph node evaluation, treatment response monitoring, and emerging radiomics applications. Across studies, diffusion-related parameters, especially the apparent diffusion coefficient (ADC) and pure molecular diffusion coefficient (D), tend to be lower in malignant cervical tissues and correlate with increased cellularity, higher tumor grade, and more aggressive disease features. IVIM metrics appear especially useful for differentiating cervical cancer from normal tissue, predicting pelvic lymph node involvement, and detecting early treatment response to chemoradiotherapy or neoadjuvant chemotherapy before substantial morphological regression occurs. In contrast, DTI remains less extensively investigated; however, preliminary findings suggest potential value for evaluating parametrial invasion, stromal disruption, tumor grade, and lymph node metastasis, particularly when integrated with IVIM-derived indices. Although diffusion-derived radiomics may further support risk stratification and treatment-response prediction, the evidence base remains limited by small cohorts, single-center designs, methodological heterogeneity, and insufficient external validation. Overall, IVIM and DTI provide valuable non-invasive insight into cervical cancer biology, but standardized acquisition protocols, reproducible thresholds, and multicenter validation are needed before routine clinical implementation.

Indexed as

cervical cancerDTIIVIM-DWIparametrial invasionprecision oncologytreatment response monitoring

Identifiers

PMID42351529
PMCPMC13297828

What OpenQuestion holds

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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.