ArticleBMC medical imaging2025
Preoperative stratification of Ki-67 proliferation index in intrahepatic mass-forming cholangiocarcinoma via tumor-halo radiomic phenotyping on biparametric MRI: a multicenter diagnostic model.
Article in BMC medical imaging, 2025. 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.
- Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.Radiation oncology (London, England) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKi-67-driven tumor proliferation influences treatment and prognosis in intrahepatic mass-forming cholangiocarcinoma (IMCC), but preoperative imaging biomarkers for Ki-67 assessment are lacking. This study explores the relationship between Magnetic resonance imaging (MRI) tumor-halo radiomic features, imaging features, and Ki-67 expression levels.
methodsThe preoperative clinical and MRI data of 151 patients with IMCC across 4 centers were analyzed in this retrospective study. The data from 108 patients across 3 centers were combined for training and validation, whereas 43 patients from the fourth center constituted the test cohort. Tumor regions of interests (ROIs) were delineated on axial images and expanded outward by 3, 5, 10, 15, and 20 mm to obtain the peritumoral ROIs with varying ranges. Features were extracted using PyRadiomics and selected through SelectFromModel combined with random forest. Models were built and validated using a K-nearest neighbor classifier and fivefold cross-validation.
resultsAge, lesion location, vascular involvement, suspicious lymph nodes, and radiologically assessed signal characteristics on T2-weighted imaging (T2WI) and Diffusion-weighted imaging (DWI) were identified as the independent predictors of Ki-67 expression in IMCC. Ultimately, the 3 mm multimodal combined model exhibited the highest predictive performance, with an area under the curve (AUC) of 0.990, 0.980, and 0.869 in the training, validation, and test cohorts, respectively.
conclusionsA combined model integrating radiomics (intra/peri-tumoral) and imaging features from biparametric MRI effectively predicts preoperative Ki-67 expression in IMCC.
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.