ArticleDigital health
PCRTA-Net: A clinical-radiological transformer-attention algorithm for preoperative prediction of pathological invasiveness in lung adenocarcinoma presenting as part-solid nodules.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Diagnosis of Suspected Pituitary Microadenomas: The Complementary Role of Habitat-based Non-enhanced MRI Radiomics Features.Journal of imaging informatics in medicine · 2026Article
- 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.Neuroradiology · 2026Article
- A multicenter-validated interpretable transformer model for pituitary microadenoma detection on non-contrast multiparametric MRI.BMC medical imaging · 2026Article
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Authors and funding
4 authors.
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Abstract
Background and objective: Despite advances in imaging, analysis of part-solid nodules (PSNs) using conventional clinical-radiological features (CRF) remains highly subjective. Therefore, we introduced a clinical-radiological transformer-attention algorithm, the Prior Clinical-Radiological Transformer-Attention Net (PCRTA-Net), and evaluated its efficacy for preoperative prediction of pathological invasiveness in lung adenocarcinoma presenting as PSNs. Methods: In this retrospective study, 277 patients with lung adenocarcinoma presenting as PSN were randomly divided into training ( Results: PCRTA-Net demonstrated superior performance over the other models, achieving an AUC of 0.9084 (95% CI: 0.8055-0.9779). It consistently showed a superior net benefit across nearly the entire range of clinically relevant threshold probabilities (3.7-95%) compared with the other models. It achieved the highest NRI and IDI in the heatmaps, confirming its incremental predictive value. Conclusion: PCRTA-Net is a diagnostic algorithm integrating CRF with TA-Net that enhances the preoperative prediction of pathological invasiveness in lung adenocarcinoma presenting as PSN. It exhibited superior diagnostic performance, potentially facilitating its application in precision medicine.
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