LIMPACAT: Multi-omics attention transformer for immune prediction in liver cancer using whole-slide imaging.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
These inferred compositions served as supervision signals to train a multiple instance learning model with an attention transformer.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
LIMPACAT exhibited ~80% accuracy in classifying immune cell levels from HCC WSIs, showing strong concordance between model prediction and deconvolution-derived estimates. These findings suggest that WSIs can serve as a proxy for immune profiling, facilitating pathology-based tumor microenvironment assessment and supporting personalized therapeutic strategies.
Characterizing the tumor immune microenvironment from histopathological images offers opportunities for ex vivo immune profiling and prognostic assessment.
APA
Chiu YJ (2026). LIMPACAT: Multi-omics attention transformer for immune prediction in liver cancer using whole-slide imaging.. PloS one, 21(1), e0339667. https://doi.org/10.1371/journal.pone.0339667
MLA
Chiu YJ. "LIMPACAT: Multi-omics attention transformer for immune prediction in liver cancer using whole-slide imaging.." PloS one, vol. 21, no. 1, 2026, pp. e0339667.
PMID
41511965 ↗
Abstract 한글 요약
Characterizing the tumor immune microenvironment from histopathological images offers opportunities for ex vivo immune profiling and prognostic assessment. However, the TCGA-LIHC dataset lacks direct immune cell composition data. Therefore, this study aims to introduce Liver Immune Microenvironment Prediction and Classification Attention Transformer (LIMPACAT), a deep learning framework that leverages whole-slide images (WSIs) to predict immune cell levels relevant to hepatocellular carcinoma (HCC) prognosis. Immune cell compositions were inferred using a deconvolution approach, with bulk RNA-seq profiles simulated from liver-specific single-cell RNA sequencing data and processed with multiple normalization methods. These inferred compositions served as supervision signals to train a multiple instance learning model with an attention transformer. LIMPACAT exhibited ~80% accuracy in classifying immune cell levels from HCC WSIs, showing strong concordance between model prediction and deconvolution-derived estimates. These findings suggest that WSIs can serve as a proxy for immune profiling, facilitating pathology-based tumor microenvironment assessment and supporting personalized therapeutic strategies.
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