A Clinical–Radiomics Nomogram for the Preoperative Prediction of Aggressive Micropapillary and a Solid Pattern in Lung Adenocarcinoma.
Background: A micropapillary pattern (MP) and solid pattern (SP) in lung adenocarcinoma (LUAD), a major subtype of non-small-cell lung cancer (NSCLC), are associated with a poor prognosis and necessitate accurate preoperative identification. This study aimed to develop and validate a predictive mode...
| Publicado en: | Current Oncology Vol. 32; no. 6; pp. 323 - 339 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
MDPI
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186205485&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186205485 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11980052 5EKK jtl: Current Oncology issn: 11980052 maglogo: N pubinfo: dt: Jun2025 vid: 32 iid: 6 pid: 97109 pub: MDPI artinfo: ui: 186205485 10.3390/curroncol32060323 186205485 ppf: 323 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Clinical–Radiomics Nomogram for the Preoperative Prediction of Aggressive Micropapillary and a Solid Pattern in Lung Adenocarcinoma. aug: au: Xie, Xiangyu Chen, Lei Li, Kun Shi, Liang Zhang, Lei Zheng, Liang affil: Department of Thoracic Surgery, The First People's Hospital of Changzhou and The Third Affiliated Hospital of Soochow University, Changzhou 213000, China sug: ab: Background: A micropapillary pattern (MP) and solid pattern (SP) in lung adenocarcinoma (LUAD), a major subtype of non-small-cell lung cancer (NSCLC), are associated with a poor prognosis and necessitate accurate preoperative identification. This study aimed to develop and validate a predictive model combining clinical and radiomics features for differentiating a high-risk MP/SP in LUAD. Methods: This retrospective study analyzed 180 surgically confirmed NSCLC patients (Stages I–IIIA), randomly divided into training (70%, n = 126) and validation (30%, n = 54) cohorts. Three prediction models were constructed: (1) a clinical model based on independent clinical and CT morphological features (e.g., nodule size, lobulation, spiculation, pleural indentation, and vascular abnormalities), (2) a radiomics model utilizing LASSO-selected features extracted using 3D Slicer, and (3) a comprehensive model integrating both clinical and radiomics data. Results: The clinical model yielded AUCs of 0.7975 (training) and 0.8462 (validation). The radiomics model showed superior performance with AUCs of 0.8896 and 0.8901, respectively. The comprehensive model achieved the highest diagnostic accuracy, with training and validation AUCs of 0.9186 and 0.9396, respectively (DeLong test, p < 0.05). Decision curve analysis demonstrated the enhanced clinical utility of the combined approach. Conclusions: Integrating clinical and radiomics features significantly improves the preoperative identification of aggressive NSCLC patterns. The comprehensive model offers a promising tool for guiding surgical and adjuvant therapy decisions. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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