Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients.
Purpose: Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients. Methods: This multi-center study retrospectively included 3...
| Publicado en: | Abdominal Radiology Vol. 49; no. 11; pp. 3780 - 3797 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2024
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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=180550638&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180550638 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Nov2024 vid: 49 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 180550638 177451094 10.1007/s00261-024-04331-7 180550638 ppf: 3780 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients. aug: au: Zhang, Jingjing Zhang, Qiang Zhao, Bo Shi, Gaofeng affil: https://ror.org/01mdjbm03 Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China sug: ab: Purpose: Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients. Methods: This multi-center study retrospectively included 322 patients diagnosed with gastric cancer from January 2013 to June 2023 at two hospitals. Handcrafted radiomics technique and the EfficientNet V2 neural network were applied to arterial, portal venous, and delayed phase CT images to extract two-dimensional handcrafted and deep learning features. A nomogram model was built by integrating the handcrafted signature, the deep learning signature, with clinical features. Discriminative ability was assessed using the receiver operating characteristics (ROC) curve and the precision-recall (P-R) curve. Model fitting was evaluated using calibration curves, and clinical utility was assessed through decision curve analysis (DCA). Results: The nomogram exhibited excellent performance. The area under the ROC curve (AUC) was 0.848 [95% confidence interval (CI), 0.793–0.893)], 0.802 (95% CI 0.688–0.889), and 0.751 (95% CI 0.652–0.833) for the training, internal validation, and external validation sets, respectively. The AUCs of the P-R curves were 0.838 (95% CI 0.756–0.895), 0.541 (95% CI 0.329–0.740), and 0.556 (95% CI 0.376–0.722) for the corresponding sets. The nomogram outperformed the clinical model and handcrafted signature across all sets (all P < 0.05). The nomogram model demonstrated good calibration and provided greater net benefit within the relevant threshold range compared to other models. Conclusion: This study created a deep learning nomogram using CECT images and clinical data to predict NAC response in LAGC patients undergoing surgical resection, offering personalized treatment insights. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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