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Novel 4D radiomics applied to dynamic FES PET images to improve prediction of breast cancer response to ER-targeted therapy.

2/5 보강

European journal of nuclear medicine and molecular imaging 📖 저널 OA 41.1% 2022~2026 2026 Vol.53(5) p. 3128-3139 OA Radiomics and Machine Learning in Me

TL;DR Metrics of tumor subregion distance and radiomic phenotype appear to perform as the best radiomic predictors for risk stratification of OS and PFS respectively by potentially reflecting characteristics of the overall tumor heterogeneity in FES PET images.

PICO 자동 추출 (휴리스틱, conf 2/4)

유사 논문

P · Population 대상 환자/모집단

환자: metastatic ER positive breast cancer treated with endocrine therapy

I · Intervention 중재 / 시술

추출되지 않음

C · Comparison 대조 / 비교

추출되지 않음

O · Outcome 결과 / 결론

Metrics of tumor subregion distance and radiomic phenotype appear to perform as the best radiomic predictors for risk stratification of OS and PFS respectively by potentially reflecting characteristics of the overall tumor heterogeneity in FES PET images. [CLINICAL TRIAL NUMBER] not applicable.

OpenAlex 토픽 · Radiomics and Machine Learning in Medical Imaging HER2/EGFR in Cancer Research Radiopharmaceutical Chemistry and Applications

Chen AW, Zeballos Torrez CR, Peterson LM, Muzi M, Specht JM, Cohen EA

📖 무료 전문 🟢 PMC 전문 PMC13013131 🔓 OA PDF oa

PubMed ↗ DOI ↗ BibTeX ↓ RIS ↓

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