{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T03:03:13Z","timestamp":1790132593507,"version":"4.0.1"},"reference-count":51,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop a multimodal model for predicting the invasiveness of ground-glass nodule (GGNs), enabling accurate differentiation of pre-invasive lesions (PIL), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The retrospective study included patients with pathologically confirmed GGNs who underwent surgical resection between May 2018 and May 2025. Univariate and multivariate logistic regression analysis were performed to screen clinical parameters, follow-up information, and CT morphological features, identifying independent predictive factors. The intratumoral region (ITR) was segmented semi-automatically. The peritumoral regions (PTRs) were extracted via 2 scaling strategies: fixed-size expansion (3\u2009mm, 6\u2009mm, 9\u2009mm, 12\u2009mm) and tumor diameter-ratio expansion (25%, 50%, 75%, 100%). Radiomics features were extracted from both ITR and PTRs. Following feature selection using the Least Absolute Shrinkage and Selection Operator (LASSO) regression, classification models were constructed based on random forest (RF), support vector machine (SVM), and logistic regression (LR). Model performance was evaluated via receiver operating characteristic (ROC) curve analysis. Four progressive integration models were further created: conventional, conventional\u2009+\u2009follow-up, conventional\u2009+\u2009follow-up\u2009+\u2009ITR, and conventional\u2009+\u2009follow-up\u2009+\u2009ITR\u2009+\u2009PTR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 517 GGNs were included in the training and internal test cohorts (63 PILs, 311 MIAs, and 143 IAs), with 91 GGNs from the external test cohort (3 PILs, 45 MIAs, 43 IAs). Maximum diameter, follow-up composite parameter, vacuole sign, and air bronchus sign were identified as independent predictive factors. Both PTR construction strategies yielded optimal models (PTR3\/PTR6 for fixed-size, PTR50%\/PTR75% for tumor diameter-ratio), demonstrating robust predictive efficacy. The RF model exhibited the highest stability. As feature dimensions expanded from conventional features to follow-up composite parameter, ITR, and PTR, the mean AUC of models exhibited a progressive increase. The full multimodal model (conventional\u2009+\u2009follow-up\u2009+\u2009ITR\u2009+\u2009PTR) achieved the best performance, with mean AUCs of 0.849 (training cohort), 0.795 (internal test cohort), and 0.838 (external test cohort).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed multimodal predictive triple-classification model, integrating conventional, follow-up, ITR, and PTR, may act as a non-invasive tool for preoperative evaluation of GGNs invasiveness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Advances in knowledge<\/jats:title>\n                    <jats:p>The study first investigated PTR model via the tumor diameter\u2014ratio method and compare it with the fixed-size method. A follow-up composite parameter (follow-up duration\u2009\u00d7\u2009follow-up changes status) was used to quantified GGN dynamic changes. The novel multimodal model, integrating conventional, follow-up, ITR, and PTR radiomics features, outperformed single-modal approaches in differentiating PIL, MIA, and IA.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjr\/tqag200","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T14:15:09Z","timestamp":1788185709000},"source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a triple-classification multimodal model based on intratumoral and peritumoral radiomic for ground-glass nodule invasiveness assessment"],"prefix":"10.1093","author":[{"given":"Yan","family":"Lv","sequence":"first","affiliation":[{"name":"Department of Radiology, Northern Jiangsu People\u2019s Hospital Affiliated to Yangzhou University & Northern Jiangsu People's Hospital , Jiangsu 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