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Ancestry-Associated Performance Variability of Open-Source AI Models for EGFR Prediction in Lung Cancer.

3/5 보강

JAMA oncology 📖 저널 OA 66.3% 2022~2026 2026 Vol.12(4) p. 402-406 cited 1 OA Radiomics and Machine Learning in Me

TL;DR Artificial intelligence-based histology tools show strong potential as rapid, low-cost adjuncts for identifying EGFR mutations; broader validation and recalibration across diverse populations and tissue types will help ensure equitable clinical adoption and maximize their impact in cancer care.

PICO 자동 추출 (휴리스틱, conf 2/4)

유사 논문

P · Population 대상 환자/모집단

2098 patients with LUAD were included (mean [SD] age, 66.

I · Intervention 중재 / 시술

추출되지 않음

C · Comparison 대조 / 비교

추출되지 않음

O · Outcome 결과 / 결론

AI-guided triage analyses showed a potential 57% reduction in rapid EGFR testing, while maintaining sensitivity of 0.84 and specificity of 0.99. [CONCLUSIONS] 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.

OpenAlex 토픽 · Radiomics and Machine Learning in Medical Imaging Lung Cancer Treatments and Mutations Lung Cancer Diagnosis and Treatment

Rakaee M, Nassar AH, Tafavvoghi M, Jabar F, Bou Farhat E, Adib E

📖 무료 전문 🟢 PMC 전문 PMC12902924 🔓 OA PDF oa

PubMed ↗ DOI ↗ BibTeX ↓ RIS ↓

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