Saddoughi Sahar A, Powell Chelsea, Stroh Gregory R, Rajagopalan Srinivasan, Bartholmai Brian J, Boland Jennifer M, Aubry Marie Christine, Harmsen William S, Blackmon Shanda H, Cassivi Stephen D, Nichols Francis C, Reisenauer Janani S, Shen K Robert, Mansfield Aaron S, Maldonado Fabien, Peikert Tobias, Wigle Dennis A
Division of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN.
Department of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN.
Mayo Clin Proc Digit Health. 2024 Jan 11;2(1):44-52. doi: 10.1016/j.mcpdig.2023.10.006. eCollection 2024 Mar.
To investigate whether an artificial intelligence (AI)-based model can predict tumor invasiveness in patients with multifocal lung adenocarcinoma (MFLA).
Patients with MFLA who underwent surgical resection were enrolled to a prospective registry trial (NCT01946100). Each identified nodule underwent retrospective computer-aided nodule assessment and risk yield (CANARY)-based AI to determine a quantitative degree of invasiveness. Data regarding age, sex, medical and surgical management, and survival were collected and analyzed. Pathologic review was performed by a pulmonary pathologist with comprehensive histologic subtyping.
From January 1, 2013, through December 31, 2018, 68 patients with MFLA underwent at least 1 surgical resection. Five-year survival for the cohort was 91%, and 10-year survival was 73.6%. No significant differences in survival were observed when separated by sex, number, or size of the nodules. A 10-year survival trend was seen when comparing patients with unilateral (100% survival) vs bilateral disease (66%). Retrospective CANARY-based AI analysis demonstrated that the majority of the nodules present at the time of diagnosis (229/302; 75.8%) were classified good, with an average score of 0.19, suggesting indolent clinical behavior and noninvasive pathology. However, AI-CANARY scores of the surgically removed nodules were significantly higher compared with those of the nonresected nodules (=.001).
The long-term survival for patients with N0, M0 MFLA who have undergone surgical resection may approach those of stage I non-small cell lung cancer. CANARY-based AI has the potential to stratify individual nodules to help guide surgical intervention versus observation of nodules.
clinicaltrials.gov Identifier: NCT01946100.
研究基于人工智能（AI）的模型能否预测多灶性肺腺癌（MFLA）患者的肿瘤侵袭性。
对接受手术切除的MFLA患者进行前瞻性注册试验（NCT01946100）。对每个识别出的结节进行回顾性计算机辅助结节评估和基于风险产量（CANARY）的人工智能分析，以确定侵袭性的定量程度。收集并分析有关年龄、性别、医疗和手术管理以及生存情况的数据。由肺病理学家进行病理检查，并进行全面的组织学亚型分类。
2013年1月1日至2018年12月31日，68例MFLA患者至少接受了1次手术切除。该队列的5年生存率为91%，10年生存率为73.6%。按性别、结节数量或大小分类时，生存情况无显著差异。比较单侧疾病（生存率100%）与双侧疾病（生存率66%）的患者时，观察到10年生存趋势。基于CANARY的人工智能回顾性分析表明，诊断时存在的大多数结节（229/302；75.8%）被分类为良好，平均评分为0.19，提示临床行为惰性和非侵袭性病理。然而，手术切除结节的人工智能CANARY评分显著高于未切除结节（P =.001）。
接受手术切除的N0、M0 MFLA患者的长期生存率可能接近I期非小细胞肺癌患者。基于CANARY的人工智能有潜力对单个结节进行分层，以帮助指导手术干预或结节观察。
clinicaltrials.gov标识符：NCT01946100。
