Dataset Information

Application of the Immunoscore as prognostic tool for hepatocellular carcinoma.

ABSTRACT: To date, the American Joint Committee on Cancer (AJCC) and the Union for International Cancer Control (UICC) tumor, nodes, metastasis (TNM) classification represents the standard system for evaluation of prognosis in solid tumors. However, the clinical outcome can be significantly different in patients with the same TNM stage. Therefore, many efforts have been made aiming to define new prognostic parameters. Indeed, analyses conducted in large cohorts of colorectal cancer patients emphasized the prognostic value of tumor-infiltrating lymphocytes, leading to the development of a prognostic score referred to as "Immunoscore". In this commentary, we recapitulate the study by Gabrielson and colleagues, recently published in Cancer Immunology Research, addressing the role of intratumoral CD3+ and CD8+ T cells as well as as prognostic markers for hepatocellular carcinoma. The authors demonstrate that Immunoscore represents a valuable prognostic marker in patients with hepatocellular carcinoma who have undergone primary tumor resection, supporting its application in a tumor setting other than colorectal cancer.

SUBMITTER: Petrizzo A

PROVIDER: S-EPMC5109780 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Application of the Immunoscore as prognostic tool for hepatocellular carcinoma.

Petrizzo Annacarmen A Buonaguro Luigi L

Journal for immunotherapy of cancer 20161115

To date, the American Joint Committee on Cancer (AJCC) and the Union for International Cancer Control (UICC) tumor, nodes, metastasis (TNM) classification represents the standard system for evaluation of prognosis in solid tumors. However, the clinical outcome can be significantly different in patients with the same TNM stage. Therefore, many efforts have been made aiming to define new prognostic parameters. Indeed, analyses conducted in large cohorts of colorectal cancer patients emphasized the ...[more]

PMID: 27879973

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Project description:Histopathological images contain rich phenotypic descriptions of the molecular processes underlying disease progression. Convolutional neural networks, state-of-the-art image analysis techniques in computer vision, automatically learn representative features from such images which can be useful for disease diagnosis, prognosis, and subtyping. Hepatocellular carcinoma (HCC) is the sixth most common type of primary liver malignancy. Despite the high mortality rate of HCC, little previous work has made use of CNN models to explore the use of histopathological images for prognosis and clinical survival prediction of HCC. We applied three pre-trained CNN models-VGG 16, Inception V3 and ResNet 50-to extract features from HCC histopathological images. Sample visualization and classification analyses based on these features showed a very clear separation between cancer and normal samples. In a univariate Cox regression analysis, 21.4% and 16% of image features on average were significantly associated with overall survival (OS) and disease-free survival (DFS), respectively. We also observed significant correlations between these features and integrated biological pathways derived from gene expression and copy number variation. Using an elastic net regularized Cox Proportional Hazards model of OS constructed from Inception image features, we obtained a concordance index (C-index) of 0.789 and a significant log-rank test (p = 7.6E-18). We also performed unsupervised classification to identify HCC subgroups from image features. The optimal two subgroups discovered using Inception model image features showed significant differences in both overall (C-index = 0.628 and p = 7.39E-07) and DFS (C-index = 0.558 and p = 0.012). Our work demonstrates the utility of extracting image features using pre-trained models by using them to build accurate prognostic models of HCC as well as highlight significant correlations between these features, clinical survival, and relevant biological pathways. Image features extracted from HCC histopathological images using the pre-trained CNN models VGG 16, Inception V3 and ResNet 50 can accurately distinguish normal and cancer samples. Furthermore, these image features are significantly correlated with survival and relevant biological pathways.

Dataset Information

Application of the Immunoscore as prognostic tool for hepatocellular carcinoma.

Publications

Application of the Immunoscore as prognostic tool for hepatocellular carcinoma.

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