ArticleNeuroradiology2026
A stacking model for AI-assisted diagnosis of suspected pituitary microadenomas on non-contrast T1COR MRI: a multicenter reader study on bridging the experience gap.
Article in Neuroradiology, 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
Abstract
objectiveThis study sought to quantify, through a multi-reader study, whether AI assistance improves diagnostic accuracy across experience levels, reduces bidirectional errors, and enhances inter-reader consensus in suspected pituitary microadenoma diagnosis. To this end, we developed and validated a stacking model integrating clinical, radiomics, and deep learning features on non-contrast T1COR MRI. MATERIALS AND
methodsThis retrospective multicenter study enrolled 636 patients from three centers, divided into training (n = 321), internal validation (n = 138), and two external validation cohorts (n = 136, n = 41). We developed four base models-handcrafted radiomics, deep transfer learning (DTL), deep learning radiomics (DLR), and clinical-and integrated them via a stacking ensemble with logistic regression as the meta-classifier. To evaluate real-world clinical impact, a three-round reader study was conducted with 315 lesions and five radiologists (three juniors, two seniors). Readers assessed non-contrast T1COR MRI unaided, with DTL assistance, and with combined model assistance. Performance metrics included AUC, accuracy, sensitivity, specificity, and inter-reader consensus.
resultsThe combined model outperformed all single-modality approaches across validation cohorts, achieving AUCs of 0.818 (EVC1) and 0.899 (EVC2) with balanced sensitivity and specificity (EVC2: 0.929 and 0.833, respectively). In the reader study with 315 lesions and five radiologists, AI assistance significantly improved diagnostic accuracy across all experience levels. With combined model assistance, gains were more pronounced: seniors achieved 75.4-79.0% accuracy in the internal validation cohort (P-adj < 0.05), and up to 85.4% in the most challenging external cohort (P-adj < 0.05)- an absolute improvement of approximately 30% over unaided performance. The combined model also enhanced diagnostic consensus, with total scores from five radiologists showing systematic improvement for both microadenoma and non-microadenoma groups.
conclusionsThe findings of this study suggest that our combined model holds promise as an effective tool to assist radiologists in the diagnosis of suspected pituitary microadenoma, providing a foundation for future clinical translation.
Indexed as
Identifiers
42622847What 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.