ArticleBMC medical imaging2026
Noninvasive preoperative prediction of perineural invasion in intrahepatic cholangiocarcinoma based on dynamic contrast-enhanced MRI.
Article in BMC medical 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.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
No grant is acknowledged in the PubMed record.
Abstract
purposeThe present study aimed to develop and validate a preoperative model based on Gd-DTPA-enhanced magnetic resonance imaging (MRI) and clinical factors for predicting perineural invasion (PNI) in patients with intrahepatic cholangiocarcinoma (ICC), enabling clinicians to perform more accurate patient evaluation and and make individualized therapeutic decisions.
methodsBetween July 2019 and February 2024, a total of 173 patients with pathologically confirmed ICC who underwent preoperative Gd‑DTPA‑enhanced MRI were retrospectively enrolled. These patients were randomly assigned to training and test cohorts at a 7:3 ratio. Multivariate logistic regression was used to identify independent predictors of PNI status and a predictive model was developed and presented as a nomogram. The model performance was assessed in terms of discrimination, calibration, and clinical utility.
resultsPeritumoral arterial hyper-enhancement (OR 14.99, 95% CI:1.95,115.22, P = 0.009), satellite nodules (OR 10.07, 95% CI: 1.34,75.48, P = 0.025), target sign on DWI (OR 0.03, 95% CI: 0.00,0.38, P = 0.007), and tumor location (OR 0.03, 95% CI: 0.00,0.28, P = 0.003) were independent risk factors to PNI status and constituted the model. The model exhibited strong discriminative ability, with an area under the curve (AUC) of 0.939 (95% CI: 0.891–0.988), sensitivity of 0.907, specificity of 0.870, and accuracy of 0.893 in the training set; corresponding values in the testing set were 0.866 (95% CI: 0.767–0.966), 0.750, 0.800, and 0.769. The decision curve analysis (DCA) curve showed that the model achieved great clinical benefits.
conclusionIn conclusion, we developed and validated a noninvasive preoperative model that integrates Gd-DTPA-enhanced MRI features to accurately predict PNI in ICC patients. This model shows significant potential to assist clinicians in preoperative risk assessment, thus facilitating improved prognostic stratification and the formulation of personalized treatment strategies.
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.