PCRTA-Net: A clinical-radiological transformer-attention algorithm for preoperative prediction of pathological...

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.

In this retrospective study, 277 patients with lung adenocarcinoma presenting as PSN were randomly divided into training (n = 194, 70%) and validation (n = 83, 30%) sets. Four diagnostic models were developed: (1) Transformer Attention Net (TA-Net), derived from computed tomography imaging analysis; (2) PCRTA-Net, integrating CRFs with TA-Net; (3) radiomics analysis based on feature engineering; and (4) CRF model utilizing multiple dimensionality reduction methods, including principal component analysis, least absolute shrinkage and selection operator, and recursive feature elimination with cross-validation. Model performance was evaluated using area under the curve (AUC), decision curve analysis, and net reclassification index (NRI) and integrated discrimination improvement (IDI) heatmaps.

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.

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.