ArticleBMC endocrine disorders2026
A multimodal study on predicting extrathyroidal extension of papillary thyroid carcinoma based on radiopathomics.
Article in BMC endocrine disorders, 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe aimed to develop and validate a radiopathomics model for predicting extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC).
methodsThis retrospective study included 388 PTC patients with preoperative ultrasound and 400× cytology images from five medical centers between June 2017 and April 2024. We analyzed ultrasound and cytology images using Python and CellProfiler to extract features. Feature selection was performed using univariate analysis, Spearman correlation, and LASSO regression. The XGBoost algorithm was then used to build radiomics, pathomics, and combined radiopathomics models. The diagnostic performance of the radiopathomics model was compared with that of radiologists in an external validation cohort. Model and radiologist performance was evaluated using the area under the receiver operating characteristic curve (AUC). The radiopathomics model was visualized and interpreted through SHAP analysis.
resultsThe radiopathomics model selected 21 features for construction. The AUC of the radiopathomics model was 0.887, 0.857, and 0.873 in the training, internal validation, and external validation cohorts, respectively, exceeding those of the single radiomics model (0.824, 0.787, and 0.804) and the pathomics model (0.809, 0.811, and 0.794). Compared with radiologists, the radiopathomics model improved the mean accuracy from 0.661 to 0.821. SHAP analysis showed that radiomics features played a major role in diagnosing ETE, while pathomics features provided additional support.
conclusionThe radiopathomics model serves as a promising auxiliary tool for preoperative ETE risk stratification and can help improve radiologists' diagnostic performance. CLINICAL TRIAL NUMBER: Not Applicable.
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.