Background: Surgical resection remains central to the curative treatment of locally advanced gastric cancer (GC), yet global variability persists in defining resectability, particularly in complex scenarios such as multivisceral invasion, positive peritoneal cytology (CY1), or oligometastatic disease. The Intercontinental Criteria of Resectability for Gastric Cancer (ICRGC) project was developed to address this gap by combining expert surgical input with artificial intelligence (AI)-based reasoning. Methods: A two-stage prospective survey was conducted during the 2024 European Gastric Cancer Association (EGCA) meeting. Fifty-eight surgical oncologists completed a 36-item questionnaire on resectability, strategy, and quality metrics. Subsequently, they reviewed AI-generated responses based on current clinical guidelines and completed a second round. Concordance between human and AI responses was classified as full, partial, or discordant, and changes in surgeon opinions were statistically analyzed. Results: Substantial agreement was observed in evidence-based domains. Seventy-nine percent of surgeons agreed with AI on distinguishing technical from oncological resectability. In cT4b cases, 61% supported restricting multivisceral resection to high-volume centers. Similar alignment was found in CY1 (54%) and N3 nodal disease (63%). Partial concordance appeared in areas requiring individualized judgment, such as peritonectomy or bulky-N disease. After AI exposure, surgeon responses shifted toward guideline-consistent decisions, including increased support for cytoreductive surgery only when CC0/1 was achievable and stricter classification of R2 resections as unresectable. Following AI exposure, 27.1% of surgeons changed at least one answer in alignment with AI recommendations, with statistically significant shifts observed in items related to surgical margin definition (p= 0.015), anatomical resection criteria (p< 0.05), and hospital stay benchmarks (p= 0.031). Conclusions: The ICRGC study demonstrates that AI-driven consensus modeling can replicate expert reasoning in complex surgical oncology and serve as a catalyst for harmonizing global practice. These findings suggest that AI-supported consensus modeling may complement expert surgical reasoning and promote greater consistency in decision-making, particularly in controversial or ambiguous cases.
背景：手术切除仍是局部进展期胃癌（GC）根治性治疗的核心，但在界定可切除性方面全球仍存在差异，尤其是在多脏器侵犯、腹腔灌洗细胞学阳性（CY1）或寡转移等复杂情况下。为弥合这一差距，胃癌可切除性洲际标准（ICRGC）项目应运而生，该项目结合了专家外科意见与基于人工智能（AI）的推理方法。 方法：在2024年欧洲胃癌协会（EGCA）会议期间开展了一项两阶段前瞻性调查。58位肿瘤外科医生完成了一份包含36个项目的问卷，内容涉及可切除性判定、治疗策略及质量指标。随后，他们审阅了基于现行临床指南生成的AI建议，并完成了第二轮问卷。将人类回答与AI回答的一致性分为完全一致、部分一致与不一致三类，并对外科医生观点的变化进行统计分析。 结果：在基于证据的领域中观察到高度一致性。79%的外科医生在区分技术可切除性与肿瘤学可切除性方面与AI意见一致。在cT4b病例中，61%的医生支持将多脏器联合切除限制在高手术量中心实施。在CY1（54%）与N3淋巴结转移（63%）方面也发现类似的一致性。在需要个体化判断的领域（如腹膜切除术或巨块型淋巴结转移）则出现部分一致。接触AI建议后，外科医生的回答更倾向于与指南一致的决定，包括：仅当能达到CC0/1减瘤程度时才更支持进行减瘤手术，以及更严格地将R2切除归类为不可切除。接触AI后，27.1%的外科医生至少改变了一项答案以符合AI建议，其中在手术切缘定义（p=0.015）、解剖性切除标准（p<0.05）及住院时间基准（p=0.031）相关项目上观察到具有统计学意义的观点转变。 结论：ICRGC研究表明，AI驱动的共识建模能够复现复杂肿瘤外科领域的专家推理逻辑，并可作为协调全球临床实践的催化剂。这些发现提示，AI支持的共识建模可补充专家外科决策思维，促进决策过程（尤其在存在争议或模糊的病例中）的更高一致性。
肿瘤(癌症)患者之家