In a study reported in JAMA Oncology, Rakaee et al identified the accuracy of open-source artificial intelligence (AI) models in predicting the presence of EGFR mutations in samples from patients with lung adenocarcinoma, including according to ancestral subgroups.
Study Details
The study included patients with lung cancer from two cohorts: Dana-Farber Cancer Institute (DFCI) from June 2013 to November 2023, and a European-based trial (TNM-I) from August 2016 to February 2022. Paired next-generation sequencing data and hematoxylin-eosin–stained whole-slide images were available for all patients. In the DFCI cohort, genetic ancestry was assigned on the basis of germline genotype data. Two open-source AI pathology models were evaluated for prediction of EGFR mutation status, measured as the area under the receiver operating characteristic curve (AUC).
Key Findings
A total of 2,098 patients were included in the analyses.
Among 1,759 patients in the DFCI cohort, EGFR mutations were detected in 432 (25%). AUCs for the two AI pathology models were 0.83 (95% confidence interval [CI] = 0.81–0.85) and 0.68 (95% CI = 0.65–0.70).
Among 339 patients in the TNM-I cohort, EGFR mutations were detected in 50 (15%). AUCs for the two respective AI models were 0.81 (95% CI = 0.74–0.88) and 0.75 (95% CI = 0.68–0.83).
In terms of ancestry, in the DCFI cohort, 54 patients were identified as African, 101 as American, 95 as Asian, and 1,465 as European. AUCs for predicting EGFR mutation for the better-performing AI model included 0.85 (95% CI = 0.72–0.94) for African ancestry, 0.68 (95% CI = 0.55–0.78) for Asian ancestry, and 0.84 (95% CI = 0.81–0.86) for European ancestry.
In analysis by tissue sample type, the AUCs for the better-performing model were 0.66 (95% CI = 0.56–0.76) for pleural specimens and 0.86 (95% CI = 0.83–0.88) for lung specimens.
Use of AI-guided triage analyses was estimated to result in a 57% reduction in rapid EGFR testing, with a sensitivity of 0.84 and a specificity of 0.99 in identifying EGFR mutations.
The investigators concluded: “This cohort study found that AI-based pathology tools may serve as preliminary adjuncts for EGFR prediction in lung cancer, though performance differences by ancestry warrant careful interpretation.”
Mehrdad Rakaee, PhD, of the Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway, is the corresponding author for the JAMA Oncology article.
DISCLOSURE: The study was supported by the Norwegian Cancer Society. For full disclosures of the study authors, visit jamanetwork.com.
