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Radiomic analysis in prediction of Human Papilloma Virus status.

ABSTRACT: Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV status. Various radiomics features were extracted from CT scans, and analyzed using statistical feature selection and prediction methods. Our approach ranked the highest in the 2016 Medical Image Computing and Computer Assisted Intervention (MICCAI) grand challenge: Oropharynx Cancer (OPC) Radiomics Challenge, Human Papilloma Virus (HPV) Status Prediction. Further analysis on the most relevant radiomic features distinguishing HPV positive and negative subjects suggested that HPV positive patients usually have smaller and simpler tumors.

SUBMITTER: Yu K

PROVIDER: S-EPMC5862639 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Radiomic analysis in prediction of Human Papilloma Virus status.

Yu Kaixian K Zhang Youyi Y Yu Yang Y Huang Chao C Liu Rongjie R Li Tengfei T Yang Liuqing L Morris Jeffrey S JS Baladandayuthapani Veerabhadran V Zhu Hongtu H

Clinical and translational radiation oncology 20171106

Human Papilloma Virus (HPV) has been associated with oropharyngeal cancer prognosis. Traditionally the HPV status is tested through invasive lab test. Recently, the rapid development of statistical image analysis techniques has enabled precise quantitative analysis of medical images. The quantitative analysis of Computed Tomography (CT) provides a non-invasive way to assess HPV status for oropharynx cancer patients. We designed a statistical radiomics approach analyzing CT images to predict HPV ...[more]

PMID: 29594229

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Project description:Interleukin-1 alpha (IL-1α) is a cytokine involved in the acute phase immune response and its expression is upregulated in a variety of solid tumors including head and neck squamous cell carcinomas (HNSCCs). Tumor expression of IL-1α is associated with increased tumor aggressiveness in HNSCCs, but this has yet to be studied in the context of human papilloma virus (HPV) status. This study is aimed at determining differences in tumor expression and subcellular localization of IL-1α in HPV-positive (HPV+) and HPV-negative (HPV-) HNSCC tumors. Tissue microarrays (TMAs) containing HPV+ (n = 31) and HPV- (n = 47) primary and metastatic HNSCCs were analyzed for IL-1α expression using immunohistochemical (IHC) staining. HPV status was confirmed using p16 IHC staining and RNA in situ hybridization (RNA ISH). Differences in IL-1α protein expression and secretion in HPV+ and HPV- HNSCC cell lines were determined by western blot and ELISA respectively. Associations between tumor IL1A expression and survival outcomes were assessed in HPV+ and HPV- HNSCC patients from publicly available gene expression datasets. Tumor expression of IL-1α was significantly increased in HPV- tumors and cell lines (as detected by IHC and western blot respectively) compared to HPV+ tumors and cell lines. There was no difference in IL-1α release between HPV+ and HPV- cell lines. IL-1α was expressed in both nuclear and cytoplasmic compartments, with predominant expression in the nucleus. Gene expression of IL1A was significantly increased in HPV-tumors/cell lines compared to HPV+ tumors/cell lines. Lastly, increased IL1A gene expression was significantly associated with worse survival in HPV- tumors but not in HPV+ tumors. Overall IL-1α expression particularly in the nucleus may possess more prognostic significance in HPV- tumors rather than HPV+ tumors. This work warrants further investigation into the role of intracellular IL-1α ligand expression in HNSCCs and may have important implications in IL-1 pathway blockade as therapeutic strategy.

Project description:To assess the role of radiomic features in distinguishing squamous and adenocarcinoma subtypes of nonsmall cell lung cancers (NSCLC) and predict EGFR mutations.Institution Review Board-approved study included chest CT scans of 93 consecutive patients (43 men, 50 women, mean age 60 ± 11 years) with biopsy-proven squamous and adenocarcinoma lung cancers greater than 1 cm. All cancers were evaluated for epidermal growth factor receptor (EGFR) mutation. The clinical parameters such as age, sex, and smoking history and standard morphology-based CT imaging features such as target lesion longest diameter (LD), longest perpendicular diameter (LPD), density, and presence of cavity were recorded. The radiomics data was obtained using commercial CT texture analysis (CTTA) software. The CTTA was performed on a single image of the dominant lung lesion. The predictive value of clinical history, standard imaging features, and radiomics was assessed with multivariable logistic regression and receiver operating characteristic (ROC) analyses.Between adenocarcinoma and squamous cell carcinomas, ROC analysis showed significant difference in 3/11 radiomic features (entropy, normalized SD, total) [AUC 0.686-0.744, P = .006 to <.0001], 1/3 clinical features (smoking) [AUC 0.732, P = .001], and 2/3 imaging features (LD and LPD) [AUC 0.646-0658, P = .020 to .032]. ROC analysis for probability variables showed higher values for radiomics (AUC 0.800, P < .0001) than clinical (AUC 0.676, P = .017) and standard imaging (AUC 0.708, P < .0001). Between EGFR mutant and wild-type adenocarcinoma, ROC analysis showed significant difference in 2/11 radiomic features (kurtosis, K2) [AUC 0.656-0.713, P = .03 to .003], 1/3 clinical features (smoking) [AUC 0.758, P < .0001]. The combined probability variable for radiomics, clinical and imaging features was higher (AUC 0.890, P < .0001) than independent probability variables.The radiomics evaluation adds incremental value to clinical history and standard imaging features in predicting histology and EGFR mutations.

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Radiomic analysis in prediction of Human Papilloma Virus status.

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Radiomic analysis in prediction of Human Papilloma Virus status.

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