Sahar A. Saddoughi MD, PhD , Chelsea Powell MD , Gregory R. Stroh MD , Srinivasan Rajagopalan PhD , Brian J. Bartholmai MD , Jennifer M. Boland MD , Marie Christine Aubry MD , William S. Harmsen MS , Shanda H. Blackmon MD, MPH , Stephen D. Cassivi MD , Francis C. Nichols MD , Janani S. Reisenauer MD , K. Robert Shen MD , Aaron S. Mansfield MD , Fabien Maldonado MD , Tobias Peikert MD , Dennis A. Wigle MD, PhD
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引用次数: 0
Abstract
Objective
To investigate whether an artificial intelligence (AI)–based model can predict tumor invasiveness in patients with multifocal lung adenocarcinoma (MFLA).
Patients and Methods
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.
Results
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 (P=.001).
Conclusion
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.
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