Impact of radiogenomics in esophageal cancer on clinical outcomes: A pilot study.
Valentina Brancato,Nunzia Garbino,Lorenzo Mannelli,Marco Aiello,Marco Salvatore,Monica Franzese,Carlo Cavaliere +6 more
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TLDR
In this paper, the authors explored the combination of CT radiomic features and molecular targets associated with clinical outcomes for characterization of ESCA patients using a correlation filter based on Spearman's correlation (ρ) and Wilcoxon-rank sum test respect to clinical outcomes.Abstract:
Background Esophageal cancer (ESCA) is the sixth most common malignancy in the world, and its incidence is rapidly increasing. Recently, several microRNAs (miRNAs) and messenger RNA (mRNA) targets were evaluated as potential biomarkers and regulators of epigenetic mechanisms involved in early diagnosis. In addition, computed tomography (CT) radiomic studies on ESCA improved the early stage identification and the prediction of response to treatment. Radiogenomics provides clinically useful prognostic predictions by linking molecular characteristics such as gene mutations and gene expression patterns of malignant tumors with medical images and could provide more opportunities in the management of patients with ESCA. Aim To explore the combination of CT radiomic features and molecular targets associated with clinical outcomes for characterization of ESCA patients. Methods Of 15 patients with diagnosed ESCA were included in this study and their CT imaging and transcriptomic data were extracted from The Cancer Imaging Archive and gene expression data from The Cancer Genome Atlas, respectively. Cancer stage, history of significant alcohol consumption and body mass index (BMI) were considered as clinical outcomes. Radiomic analysis was performed on CT images acquired after injection of contrast medium. In total, 1302 radiomics features were extracted from three-dimensional regions of interest by using PyRadiomics. Feature selection was performed using a correlation filter based on Spearman's correlation (ρ) and Wilcoxon-rank sum test respect to clinical outcomes. Radiogenomic analysis involved ρ analysis between radiomic features associated with clinical outcomes and transcriptomic signatures consisting of eight N6-methyladenosine RNA methylation regulators and five up-regulated miRNA. The significance level was set at P Results Of 25, five and 29 radiomic features survived after feature selection, considering stage, alcohol history and BMI as clinical outcomes, respectively. Radiogenomic analysis with stage as clinical outcome revealed that six of the eight mRNA regulators and two of the five up-regulated miRNA were significantly correlated with ten and three of the 25 selected radiomic features, respectively (-0.61 Conclusion Our study revealed interesting relationships between the expression of eight N6-methyladenosine RNA regulators, as well as five up-regulated miRNAs, and CT radiomic features associated with clinical outcomes of ESCA patients.read more
Citations
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The diverse role of RNA methylation in esophageal cancer
TL;DR: In this paper , the authors focus on the regulation of major RNA methylation, including m 6A, m 5C, and m 7G, and summarize how these RNA modifications affect the "life cycle" of target RNAs, including mRNA, microRNA, long non-coding RNA, and tRNA.
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The application of radiomics in esophageal cancer: Predicting the response after neoadjuvant therapy
Hai Guo,H. Tang,Wen-Long Hu,Jun-Jie Wang,Jun-jie Yang,Sen Hou,Yuhao Zuo,Zhi-qiang Deng,Hao-Ji Yan,Kai-Yuan Jiang,Heng-Ching Huang,Hai-ning Zhou,Dong Tian +12 more
TL;DR: In this article , the authors discuss the definition and workflow of radiomics, the advances in efficacy prediction after NAT, and the current application for predicting efficacy after NAT for esophageal cancer.
References
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Journal ArticleDOI
Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma
Yihuai Hu,Chenyi Xie,Hong Yang,Joshua W. K. Ho,Jing Wen,Lujun Han,Ka-On Lam,Ian Y.H. Wong,Simon Law,Keith Wan-Hang Chiu,Varut Vardhanabhuti,Jianhua Fu +11 more
TL;DR: The novel and noninvasive deep learning approach could provide efficient and accurate prediction of treatment response to nCRT in ESCC, and benefit clinical decision making of therapeutic strategy.
Journal ArticleDOI
Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma.
Yihuai Hu,Chenyi Xie,Hong Yang,Joshua W. K. Ho,Jing Wen,Lujun Han,Keith Wan-Hang Chiu,Jianhua Fu,Varut Vardhanabhuti +8 more
TL;DR: Peritumoral radiomics may provide additional predictive value for treatment response estimation in esophageal squamous cell carcinoma and thus benefit individualized therapeutic strategies.
Journal ArticleDOI
Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer.
Ruben T. H. M. Larue,Remy Klaassen,Arthur Jochems,Ralph T.H. Leijenaar,Maarten C.C.M. Hulshof,Mark I. van Berge Henegouwen,Wendy M. J. Schreurs,Meindert N. Sosef,Wouter van Elmpt,Hanneke W. M. van Laarhoven,Philippe Lambin +10 more
TL;DR: A RF model predicting 3-year overall survival based on pretreatment CT radiomic features was developed and validated in two independent datasets of esophageal cancer patients and had better prognostic power compared to the model using standard clinical variables.
Journal ArticleDOI
Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics
Xiance Jin,Xiaomin Zheng,Didi Chen,Juebin Jin,Guojie Zhu,Xia Deng,Ce Han,Changfei Gong,Yongqiang Zhou,Cong Liu,Congying Xie +10 more
TL;DR: The model with radiomic features combined with dosimetric parameters is promising and outperforms that with radiomatic features alone in predicting the treatment response of patients with EC who underwent concurrent chemoradiation (CRT).
Journal ArticleDOI
CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy
Zhining Yang,Binghui He,Binghui He,Xinyu Zhuang,Xiaoying Gao,Dandan Wang,Mei Li,Zhixiong Lin,Ren Luo +8 more
TL;DR: Three logistic regression models for pCR prediction were developed and they were able to predict pCR well in both the training and testing cohorts and they demonstrated good model performance.
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