The value of deep learning and radiomics models in predicting preoperative serosal invasion in gastric cancer: a dual-center study.
Purpose: To establish and validate a model based on deep learning (DL), integrating radiomic features with relevant clinical features to generate nomogram, for predicting preoperative serosal invasion in gastric cancer (GC). Methods: This retrospective study included 335 patients from dual centers....
| Published in: | Abdominal Radiology Vol. 50; no. 11; pp. 5090 - 5103 |
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| Main Authors: | , , , , , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Nov2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188952008&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188952008 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Nov2025 vid: 50 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 188952008 184703035 10.1007/s00261-025-04949-1 188952008 ppf: 5090 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: The value of deep learning and radiomics models in predicting preoperative serosal invasion in gastric cancer: a dual-center study. aug: au: Xu, Lihang Li, Mingyu Dong, Xianling Wang, Zhongxiao Tong, Ying Feng, Tao Xu, Shuangyan Shang, Hui Zhao, Bin Lin, Jianpeng Cao, Zhendong Zheng, Yi affil: https://ror.org/01bgds823 Radiology Department, Affiliated Hospital of Chengde Medical College, Chengde, China sug: ab: Purpose: To establish and validate a model based on deep learning (DL), integrating radiomic features with relevant clinical features to generate nomogram, for predicting preoperative serosal invasion in gastric cancer (GC). Methods: This retrospective study included 335 patients from dual centers. T staging (T1-3 or T4) was used to assess serosal invasion. Radiomic features were extracted from primary GC lesions in the venous phase CT, and DL features from 8 transfer learning models were combined to create the Hand-crafted Radiomics and Deep Learning Radiomics (HCR-DLR) model. The Clinical (CL) model was built using clinical features, and both were combined into the Clinical and Radiomics Combined (CRC) model. In total, 15 predictive models were developed using 5 machine learning algorithms. The best-performing models were visualized as nomograms. Results: The total of 14 radiomic features, 13 DL features, and 2 clinical features were considered valuable through dimensionality reduction and selection. Among the constructed models: CRC model (AUC, training cohort: 0.9212; internal test cohort: 0.8743; external test cohort: 0.8853) than HCR-DLR model (AUC, training cohort: 0.8607; internal test cohort: 0.8543; external test cohort: 0.8824) and CL model (AUC, training cohort: 0.7632; internal test cohort: 0.7219; external test cohort: 0.7294) showed better performance. A nomogram based on the logistic CL model was drawn to facilitate the usage and showed its excellent predictive performance. Conclusion: The predictive performance of the CRC Model, which integrates clinical features, radiomic features, and DL features, exhibits robust predictive capability and can serve as a simple, non-invasive, and practical tool for predicting the serosal invasion status of GC. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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