ArticleJournal of magnetic resonance imaging : JMRI2026
MRI Habitat Analysis for Preoperative Prediction of Perineural Invasion and Prognostic Stratification in Rectal Cancer.
Article in Journal of magnetic resonance imaging : JMRI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Editorial for "MRI Habitat Analysis for Preoperative Prediction of Perineural Invasion and Prognostic Stratification in Rectal Cancer".Journal of magnetic resonance imaging : JMRI · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundAccurate preoperative assessment of perineural invasion (PNI) remains challenging in rectal cancer. PURPOSE: To develop assessment models based on preoperative multiparametric MRI (mpMRI) habitat analysis for evaluating PNI status and to explore their prognostic value. STUDY TYPE: Retrospective. POPULATION: Six hundred and twenty-one rectal cancer patients were enrolled from two centers, divided into a training set (n = 330; 65.8 ± 11.22 years; 215 males), an internal validation set (in-vad, n = 152; 67.85 ± 12.43 years; 105 males), and an external validation set (ex-vad, n = 139; 62.82 ± 11.79 years; 96 males). FIELD STRENGTH/SEQUENCE: 1.5T, 3T, T2-weighted imaging using turbo spin-echo sequence, diffusion-weighted imaging using echo planar imaging, and contrast-enhanced T1-weighted imaging using 3D spoiled gradient echo sequence. ASSESSMENT: Tumor voxels were partitioned into subregions using k-means clustering, and habitat-based submodels were developed with deep learning. The Boruta algorithm combined with univariate and multivariate analyses identified key variables. STATISTICAL TESTS: Student's t test, Mann-Whitney U test, chi-square test, Boruta analysis, and DeLong's test. Significance was defined as p < 0.05. A clinical model was constructed from selected significant variables, and a nomogram integrating the clinical model with habitat-based submodels was subsequently developed.
resultsTumors were divided into three imaging-derived subregions, generating three habitat submodels. Habitat 1, 2, 3, mrN, and mrEMVI were independent PNI variables. The nomogram exhibited the highest performance, with area under the curve (AUC) values of 0.967 (95% confidence interval [CI], 0.950-0.983), 0.965 (0.941-0.990), and 0.977 (0.949-1.000) in the training, in-vad, and ex-vad sets, respectively. Kaplan-Meier analysis further confirmed its effective stratification of 3-year disease-free survival.
conclusionThe MRI-based habitat analysis model and the derived nomogram demonstrate high predictive value for preoperative assessment of PNI in rectal cancer. The nomogram also shows promising capability for prognostic risk stratification. LEVEL OF EVIDENCE: 3: TECHNICAL EFFICACY STAGE: 3.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.