Muscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival prediction
Abstract This study developed a 5-year survival prediction model for gastric cancer patients by combining radiomics and deep learning, focusing on CT-based 2D and 3D features of the iliopsoas and erector spinae muscles. Retrospective data from 705 patients across two centers were analyzed, with clin...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-09083-y |
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| _version_ | 1849238560138330112 |
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| author | Yuan Hong Peng Zhang Zhijun Teng Kang Cheng Zimo Zhang Yixian Cheng Guodong Cao Bo Chen |
| author_facet | Yuan Hong Peng Zhang Zhijun Teng Kang Cheng Zimo Zhang Yixian Cheng Guodong Cao Bo Chen |
| author_sort | Yuan Hong |
| collection | DOAJ |
| description | Abstract This study developed a 5-year survival prediction model for gastric cancer patients by combining radiomics and deep learning, focusing on CT-based 2D and 3D features of the iliopsoas and erector spinae muscles. Retrospective data from 705 patients across two centers were analyzed, with clinical variables assessed via Cox regression and radiomic features extracted using deep learning. The 2D model outperformed the 3D approach, leading to feature fusion across five dimensions, optimized via logistic regression. Results showed no significant association between clinical baseline characteristics and survival, but the 2D model demonstrated strong prognostic performance (AUC ~ 0.8), with attention heatmaps emphasizing spinal muscle regions. The 3D model underperformed due to irrelevant data. The final integrated model achieved stable predictive accuracy, confirming the link between muscle mass and survival. This approach advances precision medicine by enabling personalized prognosis and exploring 3D imaging feasibility, offering insights for gastric cancer research. |
| format | Article |
| id | doaj-art-9ec8687c85a84c9dbfe68d765adade23 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9ec8687c85a84c9dbfe68d765adade232025-08-20T04:01:34ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-09083-yMuscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival predictionYuan Hong0Peng Zhang1Zhijun Teng2Kang Cheng3Zimo Zhang4Yixian Cheng5Guodong Cao6Bo Chen7Department of General Surgery, The First Affiliated Hospital of Anhui Medical UniversityDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical UniversityDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical UniversityDepartment of The First Clinical Medical College, Anhui Medical UniversityDepartment of The First Clinical Medical College, Anhui Medical UniversityDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical UniversityDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical UniversityDepartment of General Surgery, The First Affiliated Hospital of Anhui Medical UniversityAbstract This study developed a 5-year survival prediction model for gastric cancer patients by combining radiomics and deep learning, focusing on CT-based 2D and 3D features of the iliopsoas and erector spinae muscles. Retrospective data from 705 patients across two centers were analyzed, with clinical variables assessed via Cox regression and radiomic features extracted using deep learning. The 2D model outperformed the 3D approach, leading to feature fusion across five dimensions, optimized via logistic regression. Results showed no significant association between clinical baseline characteristics and survival, but the 2D model demonstrated strong prognostic performance (AUC ~ 0.8), with attention heatmaps emphasizing spinal muscle regions. The 3D model underperformed due to irrelevant data. The final integrated model achieved stable predictive accuracy, confirming the link between muscle mass and survival. This approach advances precision medicine by enabling personalized prognosis and exploring 3D imaging feasibility, offering insights for gastric cancer research.https://doi.org/10.1038/s41598-025-09083-ySarcopeniaGastric cancerRadical gastrectomyDeep learning |
| spellingShingle | Yuan Hong Peng Zhang Zhijun Teng Kang Cheng Zimo Zhang Yixian Cheng Guodong Cao Bo Chen Muscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival prediction Scientific Reports Sarcopenia Gastric cancer Radical gastrectomy Deep learning |
| title | Muscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival prediction |
| title_full | Muscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival prediction |
| title_fullStr | Muscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival prediction |
| title_full_unstemmed | Muscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival prediction |
| title_short | Muscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival prediction |
| title_sort | muscle driven prognostication in gastric cancer a multicenter deep learning framework integrating iliopsoas and erector spinae radiomics for 5 year survival prediction |
| topic | Sarcopenia Gastric cancer Radical gastrectomy Deep learning |
| url | https://doi.org/10.1038/s41598-025-09083-y |
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