本研究旨在开发和验证基于机器学习的多模态融合 (MMF) 模型，使用 18F-氟脱氧葡萄糖 (FDG) PET/CT 放射组学和核支持张量机 (KSTM)，并结合临床因素和核医学专家的诊断单独预测晚期胃癌 (AGC) 的腹膜转移 (PM)。2006 年 11 月至 2020 年 9 月期间，共有 167 名接受术前 PET/CT 和后续手术的患者被纳入研究，并分为训练和测试队列。 PM状态通过腹腔镜探查和术后病理证实。 PET/CT 特征是通过经典的放射组学、基于手工特征的模型和基于 KSTM 自学习的模型构建的。临床列线图由 PM 的独立危险因素构建。最后，使用证据推理将 PET/CT 特征、临床列线图和专家诊断融合起来，建立 MMF 模型。MMF 模型在两个队列中都表现出优异的性能（训练中曲线下面积 [AUC] 94.16% 和 90.84%）和测试），并表现出比临床列线图或专家诊断更好的预测准确性（净重分类改进 p < 0.05）。 MMF模型也具有令人满意的泛化能力，即使是在18F-FDG摄取较差的粘液腺癌和印戒细胞癌中（训练和测试中AUC分别为97.98%和89.71%）。基于18F-FDG PET/CT放射组学的MMF该模型对于预测 AGC 中的 PM 可能具有重要的临床意义，表明有必要结合不同方式的信息来综合预测 PM。© 2024。外科肿瘤学会。

This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using 18F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nuclear medicine experts' diagnoses to individually predict peritoneal metastasis (PM) in advanced gastric cancer (AGC).A total of 167 patients receiving preoperative PET/CT and subsequent surgery were included between November 2006 and September 2020 and were divided into a training and testing cohort. The PM status was confirmed via laparoscopic exploration and postoperative pathology. The PET/CT signatures were constructed by classic radiomic, handcrafted-feature-based model and KSTM self-learning-based model. The clinical nomogram was constructed by independent risk factors for PM. Lastly, the PET/CT signatures, clinical nomogram, and experts' diagnoses were fused using evidential reasoning to establish the MMF model.The MMF model showed excellent performance in both cohorts (area under the curve [AUC] 94.16% and 90.84% in training and testing), and demonstrated better prediction accuracy than clinical nomogram or experts' diagnoses (net reclassification improvement p < 0.05). The MMF model also had satisfactory generalization ability, even in mucinous adenocarcinoma and signet ring cell carcinoma which have poor uptake of 18F-FDG (AUC 97.98% and 89.71% in training and testing).The 18F-FDG PET/CT radiomics-based MMF model may have significant clinical implications in predicting PM in AGC, revealing that it is necessary to combine the information from different modalities for comprehensive prediction of PM.© 2024. Society of Surgical Oncology.