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Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma
SIMPLE SUMMARY: Hepatocellular carcinoma is a widespread cancer with complex molecular heterogeneity. Compared with invasive tissue sampling, the radiomics framework shows promise in non-invasively decoding tumor heterogeneity. In this study, we utilized integrative analysis of radiomics and genomic...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137067/ https://www.ncbi.nlm.nih.gov/pubmed/37190266 http://dx.doi.org/10.3390/cancers15082338 |
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author | Gu, Yu Huang, Hao Tong, Qi Cao, Meng Ming, Wenlong Zhang, Rongxin Zhu, Wenyong Wang, Yuqi Sun, Xiao |
author_facet | Gu, Yu Huang, Hao Tong, Qi Cao, Meng Ming, Wenlong Zhang, Rongxin Zhu, Wenyong Wang, Yuqi Sun, Xiao |
author_sort | Gu, Yu |
collection | PubMed |
description | SIMPLE SUMMARY: Hepatocellular carcinoma is a widespread cancer with complex molecular heterogeneity. Compared with invasive tissue sampling, the radiomics framework shows promise in non-invasively decoding tumor heterogeneity. In this study, we utilized integrative analysis of radiomics and genomics profiles to characterize hepatocellular carcinoma inter-tumor and intra-tumor heterogeneity. We extracted multi-view imaging features from contrast-enhanced CT scans, and fused features for potential radiomics subtypes identification. Differentiated immune pathway activity and inflammatory tumor microenvironment between subtypes were obtained, and the predominant radiogenomics association between texture-related and immune-related was demonstrated and validated in independent cohorts. These findings could provide clues for non-invasive inflammation-based risk stratification in hepatocellular carcinoma. ABSTRACT: Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies worldwide, and the pronounced intra- and inter-tumor heterogeneity restricts clinical benefits. Dissecting molecular heterogeneity in HCC is commonly explored by endoscopic biopsy or surgical forceps, but invasive tissue sampling and possible complications limit the broadeer adoption. The radiomics framework is a promising non-invasive strategy for tumor heterogeneity decoding, and the linkage between radiomics and immuno-oncological characteristics is worth further in-depth study. In this study, we extracted multi-view imaging features from contrast-enhanced CT (CE-CT) scans of HCC patients, followed by developing a fused imaging feature subtyping (FIFS) model to identify two distinct radiomics subtypes. We observed two subtypes of patients with distinct texture-dominated radiomics profiles and prognostic outcomes, and the radiomics subtype identified by FIFS model was an independent prognostic factor. The heterogeneity was mainly attributed to inflammatory pathway activity and the tumor immune microenvironment. The predominant radiogenomics association was identified between texture-related features and immune-related pathways by integrating network analysis, and was validated in two independent cohorts. Collectively, this work described the close connections between multi-view radiomics features and immuno-oncological characteristics in HCC, and our integrative radiogenomics analysis strategy may provide clues to non-invasive inflammation-based risk stratification. |
format | Online Article Text |
id | pubmed-10137067 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101370672023-04-28 Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma Gu, Yu Huang, Hao Tong, Qi Cao, Meng Ming, Wenlong Zhang, Rongxin Zhu, Wenyong Wang, Yuqi Sun, Xiao Cancers (Basel) Article SIMPLE SUMMARY: Hepatocellular carcinoma is a widespread cancer with complex molecular heterogeneity. Compared with invasive tissue sampling, the radiomics framework shows promise in non-invasively decoding tumor heterogeneity. In this study, we utilized integrative analysis of radiomics and genomics profiles to characterize hepatocellular carcinoma inter-tumor and intra-tumor heterogeneity. We extracted multi-view imaging features from contrast-enhanced CT scans, and fused features for potential radiomics subtypes identification. Differentiated immune pathway activity and inflammatory tumor microenvironment between subtypes were obtained, and the predominant radiogenomics association between texture-related and immune-related was demonstrated and validated in independent cohorts. These findings could provide clues for non-invasive inflammation-based risk stratification in hepatocellular carcinoma. ABSTRACT: Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies worldwide, and the pronounced intra- and inter-tumor heterogeneity restricts clinical benefits. Dissecting molecular heterogeneity in HCC is commonly explored by endoscopic biopsy or surgical forceps, but invasive tissue sampling and possible complications limit the broadeer adoption. The radiomics framework is a promising non-invasive strategy for tumor heterogeneity decoding, and the linkage between radiomics and immuno-oncological characteristics is worth further in-depth study. In this study, we extracted multi-view imaging features from contrast-enhanced CT (CE-CT) scans of HCC patients, followed by developing a fused imaging feature subtyping (FIFS) model to identify two distinct radiomics subtypes. We observed two subtypes of patients with distinct texture-dominated radiomics profiles and prognostic outcomes, and the radiomics subtype identified by FIFS model was an independent prognostic factor. The heterogeneity was mainly attributed to inflammatory pathway activity and the tumor immune microenvironment. The predominant radiogenomics association was identified between texture-related features and immune-related pathways by integrating network analysis, and was validated in two independent cohorts. Collectively, this work described the close connections between multi-view radiomics features and immuno-oncological characteristics in HCC, and our integrative radiogenomics analysis strategy may provide clues to non-invasive inflammation-based risk stratification. MDPI 2023-04-17 /pmc/articles/PMC10137067/ /pubmed/37190266 http://dx.doi.org/10.3390/cancers15082338 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gu, Yu Huang, Hao Tong, Qi Cao, Meng Ming, Wenlong Zhang, Rongxin Zhu, Wenyong Wang, Yuqi Sun, Xiao Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma |
title | Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma |
title_full | Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma |
title_fullStr | Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma |
title_full_unstemmed | Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma |
title_short | Multi-View Radiomics Feature Fusion Reveals Distinct Immuno-Oncological Characteristics and Clinical Prognoses in Hepatocellular Carcinoma |
title_sort | multi-view radiomics feature fusion reveals distinct immuno-oncological characteristics and clinical prognoses in hepatocellular carcinoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137067/ https://www.ncbi.nlm.nih.gov/pubmed/37190266 http://dx.doi.org/10.3390/cancers15082338 |
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