ArticleJournal of thoracic disease2026
GAN-generated target reconstruction CT radiomics for prediction of the novel IASLC grading of pulmonary adenocarcinoma.
Article in Journal of thoracic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The novel International Association for the Study of Lung Cancer (IASLC) pathologic grading system provides a better stratification of prognosis. This study aims to develop a radiomics model based on the generative-adversarial-network (GAN)-generated target reconstruction computed tomography (GTRCT) for the preoperative prediction of IASLC grading and to compare that with the normal-resolution computed tomography (NRCT). Methods: This retrospective study included patients pathologically diagnosed with invasive pulmonary adenocarcinoma (IPA) from July 2018 to December 2021 from Changzheng Hospital and The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University. Among them, 122 patients from Hospital 2 served as a separate external validation set. By extracting image features and using least absolute shrinkage and selection operator to screen the features, radiomics models were constructed to predict the novel IASLC grading of IPA, and the model performance of GTRCT and NRCT was compared. The diagnostic performance of the models was evaluated using receiver operating characteristic curves (ROC), accuracy, sensitivity, and specificity. In addition, calibration curves and decision curve analysis (DCA) were applied to validate two different models. Results: This retrospective study included 396 patients (mean age: 60.41±10.35 years; male: 184) from the two hospitals. Radiomics models based on GTRCT and NRCT both demonstrated good predictive performance for pathological grading. The GTRCT-based prediction model achieved area under the curve (AUC) of 0.943, 0.942, and 0.844 in the training, internal validation, and external validation sets, respectively, compared with 0.92, 0.935 and 0.751 for the NRCT-based model. Calibration and Brier scores confirmed good agreement, the decision curve image showed a positive net benefit for the GTRCT-based model. Conclusions: The GTRCT-based radiomics model exhibits good accuracy in predicting the novel IASLC grading of IPA and achieves superior performance compared to the NRCT-based model. It thus helps evaluate patient prognosis and offers more comprehensive preoperative guidance for clinical treatment selection.
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