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Multimodal Artificial Intelligence-Based Virtual Biopsy for Diagnosing Abdominal Lavage Cytology-Positive Gastric Cancer

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机构: [1]The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China. [2]Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China. [3]Big data analysis and mining application for precise diagnosis and treatment of gastric cancer Hebei Provincial Engineering Research Center, Shijiazhuang, 050011, China. [4]School of Chinese Medicine &amp School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, China. [5]Department of Gastroenterology and Hepatology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, China. [6]Department of General Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430065, China. [7]Department of General Surgery, Shijiazhuang People's Hospital, Shijiazhuang, Hebei, 050050, China. [8]Department of General Surgery, Baoding Central Hospital, Baoding, Hebei, 071030, China. [9]General Surgery Department, Hengshui People's Hospital, Hengshui, Hebei, 053099, China. [10]Research Center and Tumor Research Institute, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
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关键词: gastric cancer multimodal artificial intelligence peritoneal lavage cytology-positive (CY1) radiomics virtual biopsy

摘要:
Gastric cancer with peritoneal dissemination remains a significant clinical challenge due to its poor prognosis and difficulty in early detection. This study introduces a multimodal artificial intelligence-based risk stratification assessment (RSA) model, integrating radiomic and clinical data to predict peritoneal lavage cytology-positive (GC-CY1) in gastric cancer patients. The RSA model is trained and validated across retrospective, external, and prospective cohorts. In the training cohort, the RSA model achieved an area under the curve (AUC) of 0.866, outperforming traditional clinical and radiomic feature models. External validation cohorts confirmed its robustness, with AUC values of 0.883 and 0.823 for predicting peritoneal metastasis and recurrence, respectively. In a prospective validation involving 152 patients, the model maintained superior predictive performance (AUC = 0.835). The RSA model also demonstrated significant clinical benefits by effectively identifying high-risk patients likely to benefit from specific treatments, such as paclitaxel-based conversion therapy. These findings suggest that the RSA model offers a reliable, non-invasive diagnostic tool for gastric cancer, capable of improving early detection and treatment outcomes. Further prospective studies are warranted to explore its full clinical potential.© 2025 The Author(s). Advanced Science published by Wiley‐VCH GmbH.

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出版当年[2025]版:
大类 | 1 区 综合性期刊
小类 | 1 区 化学:综合 1 区 材料科学:综合 1 区 纳米科技
最新[2025]版:
大类 | 1 区 综合性期刊
小类 | 1 区 化学:综合 1 区 材料科学:综合 1 区 纳米科技
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出版当年[2024]版:
Q1 CHEMISTRY, MULTIDISCIPLINARY Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY Q1 NANOSCIENCE & NANOTECHNOLOGY
最新[2024]版:
Q1 CHEMISTRY, MULTIDISCIPLINARY Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY Q1 NANOSCIENCE & NANOTECHNOLOGY

影响因子: 最新[2024版] 最新五年平均 出版当年[2025版] 出版当年五年平均 出版前一年[2024版]

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第一作者机构: [1]The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China. [2]Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China. [3]Big data analysis and mining application for precise diagnosis and treatment of gastric cancer Hebei Provincial Engineering Research Center, Shijiazhuang, 050011, China.
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通讯机构: [1]The Third Department of Surgery, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China. [2]Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer, Shijiazhuang, 050011, China. [3]Big data analysis and mining application for precise diagnosis and treatment of gastric cancer Hebei Provincial Engineering Research Center, Shijiazhuang, 050011, China. [10]Research Center and Tumor Research Institute, the Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
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