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Habitat radiomics and deep learning on gadoxetic acid-enhanced MRI for noninvasive assessment of CK19 expression and recurrence-free survival in hepatocellular carcinoma.

1/5 보강

Frontiers in oncology 📖 저널 OA 100% 2021~2026 2025 Vol.15() p. 1684264

PICO 자동 추출 (휴리스틱, conf 2/4)

P · Population 대상 환자/모집단

539 patients with HCC from two hospitals were divided into training (n = 266), internal (n = 114), and external (n = 159) test sets.

I · Intervention 중재 / 시술

추출되지 않음

C · Comparison 대조 / 비교

추출되지 않음

O · Outcome 결과 / 결론

RFS was significantly different between the DL-HR nomogram model-predicted CK19+ and CK19- HCC patients across all sets (all P < 0.05). [CONCLUSIONS] The DL-HR nomogram model integrating clinical, radiologic, habitat radiomics, and deep learning features effectively predicted the CK19 expression and served as an effective tool for RFS risk stratification in HCC.

Chen W, Hu J, Du M, Zhang T, Gu C, Wu Q

📖 무료 전문 🟢 PMC 전문 PMC12641396

PubMed ↗ DOI ↗ BibTeX ↓ RIS ↓

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