Literature DB >> 33387095

Radiogenomics in Interventional Oncology.

Amgad M Moussa1, Etay Ziv2.   

Abstract

PURPOSE OF REVIEW: Radiogenomics is a growing field that has garnered immense interest over the past decade, owing to its numerous applications in the field of oncology and its potential value in improving patient outcomes. Current applications have only begun to delve into the potential of radiogenomics, and particularly in interventional oncology, there is room for development and increased value of these applications. RECENT
FINDINGS: The field of interventional oncology (IO) has seen valuable radiogenomic applications, from prediction of response to locoregional therapies in hepatocellular carcinoma to identification of genetic mutations in non-small cell lung cancer. Future directions that can increase the value of radiogenomics include applications that address tumor heterogeneity, predict immune responsiveness of tumors, and differentiate between oligoprogression and early widespread progression, among others. Radiogenomics, whether in terms of methodologies or applications, is still in the early stages of development and far from maturation. Future applications, particularly in the field of interventional oncology, will allow realization of its full potential.

Entities:  

Keywords:  Artificial intelligence; Hepatocellular carcinoma; Interventional oncology; Non-small cell lung cancer; Radiogenomics; Radiomics

Mesh:

Year:  2021        PMID: 33387095     DOI: 10.1007/s11912-020-00994-9

Source DB:  PubMed          Journal:  Curr Oncol Rep        ISSN: 1523-3790            Impact factor:   5.075


  31 in total

Review 1.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

Review 2.  Radiomics and radiogenomics in lung cancer: A review for the clinician.

Authors:  Rajat Thawani; Michael McLane; Niha Beig; Soumya Ghose; Prateek Prasanna; Vamsidhar Velcheti; Anant Madabhushi
Journal:  Lung Cancer       Date:  2017-11-08       Impact factor: 5.705

3.  Radiogenomics: what it is and why it is important.

Authors:  Maciej A Mazurowski
Journal:  J Am Coll Radiol       Date:  2015-08       Impact factor: 5.532

Review 4.  The Barcelona approach: diagnosis, staging, and treatment of hepatocellular carcinoma.

Authors:  Josep M Llovet; Josep Fuster; Jordi Bruix
Journal:  Liver Transpl       Date:  2004-02       Impact factor: 5.799

5.  Gene expression patterns in human liver cancers.

Authors:  Xin Chen; Siu Tim Cheung; Samuel So; Sheung Tat Fan; Christopher Barry; John Higgins; Kin-Man Lai; Jiafu Ji; Sandrine Dudoit; Irene O L Ng; Matt Van De Rijn; David Botstein; Patrick O Brown
Journal:  Mol Biol Cell       Date:  2002-06       Impact factor: 4.138

6.  Tumor invasiveness and prognosis in resected hepatocellular carcinoma. Clinical and pathogenetic implications.

Authors:  H C Hsu; T T Wu; M Z Wu; J C Sheu; C S Lee; D S Chen
Journal:  Cancer       Date:  1988-05-15       Impact factor: 6.860

7.  Independent validation of genes and polymorphisms reported to be associated with radiation toxicity: a prospective analysis study.

Authors:  Gillian C Barnett; Charlotte E Coles; Rebecca M Elliott; Caroline Baynes; Craig Luccarini; Don Conroy; Jennifer S Wilkinson; Jonathan Tyrer; Vivek Misra; Radka Platte; Sarah L Gulliford; Matthew R Sydes; Emma Hall; Søren M Bentzen; David P Dearnaley; Neil G Burnet; Paul D P Pharoah; Alison M Dunning; Catharine M L West
Journal:  Lancet Oncol       Date:  2011-12-12       Impact factor: 41.316

8.  A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma.

Authors:  Sudeep Banerjee; David S Wang; Hyun J Kim; Claude B Sirlin; Michael G Chan; Ronald L Korn; Aaron M Rutman; Surachate Siripongsakun; David Lu; Galym Imanbayev; Michael D Kuo
Journal:  Hepatology       Date:  2015-07-01       Impact factor: 17.425

9.  Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings.

Authors:  Lin Lu; Ross C Ehmke; Lawrence H Schwartz; Binsheng Zhao
Journal:  PLoS One       Date:  2016-12-29       Impact factor: 3.240

10.  Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results.

Authors:  Bachir Taouli; Yujin Hoshida; Suguru Kakite; Xintong Chen; Poh Seng Tan; Xiaochen Sun; Shingo Kihira; Kensuke Kojima; Sara Toffanin; M Isabel Fiel; Hadassa Hirschfield; Mathilde Wagner; Josep M Llovet
Journal:  Eur Radiol       Date:  2017-04-24       Impact factor: 7.034

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  4 in total

Review 1.  Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.

Authors:  Sanjay Saxena; Biswajit Jena; Neha Gupta; Suchismita Das; Deepaneeta Sarmah; Pallab Bhattacharya; Tanmay Nath; Sudip Paul; Mostafa M Fouda; Manudeep Kalra; Luca Saba; Gyan Pareek; Jasjit S Suri
Journal:  Cancers (Basel)       Date:  2022-06-09       Impact factor: 6.575

Review 2.  Role of Image-Guided Percutaneous Needle Biopsy in the Age of Precision Medicine.

Authors:  Miyuki Sone; Shunsuke Sugawara; Yasushi Yatabe
Journal:  Curr Oncol Rep       Date:  2022-04-01       Impact factor: 5.945

3.  A radiogenomics biomarker based on immunological heterogeneity for non-invasive prognosis of renal clear cell carcinoma.

Authors:  Jiahao Gao; Fangdie Ye; Fang Han; Haowen Jiang; Jiawen Zhang
Journal:  Front Immunol       Date:  2022-09-13       Impact factor: 8.786

4.  A radiogenomics application for prognostic profiling of endometrial cancer.

Authors:  Erling A Hoivik; Erlend Hodneland; Julie A Dybvik; Kari S Wagner-Larsen; Kristine E Fasmer; Hege F Berg; Mari K Halle; Ingfrid S Haldorsen; Camilla Krakstad
Journal:  Commun Biol       Date:  2021-12-06
  4 in total

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