[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3891":3,"related-3891":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":37,"slug":38,"view_count":39,"doi":40,"paper":41,"created_at":72},3891,"Transformer‐Based PET\u002FCT Fusion Enables Preoperative Prediction and Prognostic Stratification of Spread Through Air Spaces in Lung Adenocarcinoma","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fadvs.78076","Spread through air spaces (STAS) is associated with recurrence and unfavorable outcomes in lung adenocarcinoma, yet reliable preoperative identification remains challenging. This multicenter retrospective study includes 212 patients with 219 tumors across three institutions and develops a transformer-based multimodal framework integrating eight complementary 2.5D PET and CT representations for preoperative STAS risk assessment. The model achieves an area under the receiver operating characteristic curve of 0.822 (95% CI, 0.725-0.920) in the independent external test cohort, with higher discrimination than the evaluated single-modality and conventional fusion approaches. Beyond STAS classification, the continuous model-derived risk score is associated with progression-free survival after adjustment for pathological T stage, N stage, and maximum tumor diameter (HR, 4.24; 95% CI, 1.47-12.25; P = 0.008), although the limited number of progression events warrants cautious interpretation. Exploratory transcriptomic analyses across two public cohorts further nominate candidate programs involving cell adhesion, vesicle trafficking, complement activity, and metabolic remodeling. Together, these findings highlight the potential of multimodal PET\u002FCT fusion to integrate morphological and metabolic information for noninvasive STAS risk stratification and support further prospective multicenter evaluation toward individualized surgical decision support.","气腔播散（STAS）与肺腺癌复发及不良预后相关，但可靠的术前识别仍具挑战性。这项多中心回顾性研究纳入三家机构的212例患者共219个肿瘤，开发了一个基于Transformer的多模态框架，整合八种互补的2.5D PET和CT表征，用于术前STAS风险评估。该模型在独立外部测试队列中达到受试者工作特征曲线下面积0.822（95% CI，0.725-0.920），其区分能力优于所评估的单模态和常规融合方法。除STAS分类外，经病理T分期、N分期和最大肿瘤直径调整后，模型衍生的连续风险评分与无进展生存期相关（HR，4.24；95% CI，1.47-12.25；P = 0.008），尽管进展事件数量有限，需谨慎解读。在两个公共队列中进行的探索性转录组分析进一步提名了涉及细胞黏附、囊泡运输、补体活性和代谢重塑的候选程序。总之，这些发现凸显了多模态PET\u002FCT融合整合形态学和代谢信息用于无创STAS风险分层的潜力，并支持进一步开展前瞻性多中心评估，以推动个体化手术决策支持。",null,"Advanced Science","2026-09-29T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文为肺腺癌PET\u002FCT影像AI预测研究，属医学影像领域，与三农、农业信息化、智慧农业无关，不建议进入每日精选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":36},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":33,"tags":35},"I2",3,true,[],[34],"decision support",[],"农业相关性需要复核",[],"Transformer‐BasedPETCTFusionEnablesPreop-3891",2,"10.1002\u002Fadvs.78076",{"doi":40,"openalex_id":42,"authors":43,"venue":10,"cited_by_count":15,"oa_url":63,"card":64,"direction":70,"ingested_from":71},"W7214770677",[44,46,49,51,53,55,57,60],{"name":45,"orcid":9},"Xin‐Yu Zhu",{"name":47,"orcid":48},"Meng Meng","https:\u002F\u002Forcid.org\u002F0000-0002-6499-5452",{"name":50,"orcid":9},"Zhen‐Zhen Wang",{"name":52,"orcid":9},"Deng‐Lu Lu",{"name":54,"orcid":9},"Ya‐Min Wei",{"name":56,"orcid":9},"Xu‐Chang Mi",{"name":58,"orcid":59},"Xingyu Mu","https:\u002F\u002Forcid.org\u002F0000-0002-0032-0448",{"name":61,"orcid":62},"Fu Wei","https:\u002F\u002Forcid.org\u002F0009-0000-5173-6331","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fadvs.78076",{"tldr":65,"method":66,"finding":67,"direction":68,"opportunity":69},"用Transformer融合PET\u002FCT多模态影像，术前预测肺腺癌气腔播散并分层预后。","212例多中心数据，8种2.5D PET\u002FCT表征，Transformer多模态","外部测试AUC达0.822，风险评分与无进展生存相关（HR 4.24），优于单模态与常规融合。","农业人工智能与决策模型","该多模态融合与连续风险评分思路可迁移至作物病害\u002F表型预测，探索多源影像融合与预后分层建模。","农业遥感与作物表型","openalex","2026-10-01T23:30:19.652816Z",{"total":15,"page":74,"page_size":74,"items":75},1,[]]