Literature DB >> 33521373

Radiomics in lung cancer for oncologists.

Carolina de la Pinta1, Nuria Barrios-Campo2, David Sevillano3.   

Abstract

Radiomics has revolutionized the world of medical imaging. The aim of this review is to guide oncologists in radiomics and its applications in diagnosis, prediction of response and damage, prediction of survival, and prognosis in lung cancer. In this review, we analyzed published literature on PubMed and MEDLINE with papers published in the last 10 years. We included papers in English language with information about radiomics features and diagnostic, predictive, and prognosis of radiomics in lung cancer. All citations were evaluated for relevant content and validation. RELEVANCE FOR PATIENTS: The evolution of technology allows the development of computer algorithms that facilitate the diagnosis and evaluation of response after different oncological treatments and their non-invasive follow-up. Copyright: © Whioce Publishing Pte. Ltd.

Entities:  

Keywords:  diagnosis and response; lung cancer; radiogenomics; radiomics

Year:  2020        PMID： 33521373      PMCID： PMC7837741     

Source DB:  PubMed          Journal:  J Clin Transl Res        ISSN： 2382-6533


  65 in total

1.  Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival.

Authors:  Balaji Ganeshan; Elleny Panayiotou; Kate Burnand; Sabina Dizdarevic; Ken Miles
Journal:  Eur Radiol       Date:  2011-11-17       Impact factor: 5.315

2.  Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development.

Authors:  Alexandra Cunliffe; Samuel G Armato; Richard Castillo; Ngoc Pham; Thomas Guerrero; Hania A Al-Hallaq
Journal:  Int J Radiat Oncol Biol Phys       Date:  2015-02-07       Impact factor: 7.038

3.  Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.

Authors:  Yoganand Balagurunathan; Yuhua Gu; Hua Wang; Virendra Kumar; Olya Grove; Sam Hawkins; Jongphil Kim; Dmitry B Goldgof; Lawrence O Hall; Robert A Gatenby; Robert J Gillies
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

4.  Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy?

Authors:  Gary J R Cook; Connie Yip; Muhammad Siddique; Vicky Goh; Sugama Chicklore; Arunabha Roy; Paul Marsden; Shahreen Ahmad; David Landau
Journal:  J Nucl Med       Date:  2012-11-30       Impact factor: 10.057

5.  Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer.

Authors:  David V Fried; Susan L Tucker; Shouhao Zhou; Zhongxing Liao; Osama Mawlawi; Geoffrey Ibbott; Laurence E Court
Journal:  Int J Radiat Oncol Biol Phys       Date:  2014-09-11       Impact factor: 7.038

6.  Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma.

Authors:  Olya Grove; Anders E Berglund; Matthew B Schabath; Hugo J W L Aerts; Andre Dekker; Hua Wang; Emmanuel Rios Velazquez; Philippe Lambin; Yuhua Gu; Yoganand Balagurunathan; Edward Eikman; Robert A Gatenby; Steven Eschrich; Robert J Gillies
Journal:  PLoS One       Date:  2015-03-04       Impact factor: 3.240

7.  Early Change in Metabolic Tumor Heterogeneity during Chemoradiotherapy and Its Prognostic Value for Patients with Locally Advanced Non-Small Cell Lung Cancer.

Authors:  Xinzhe Dong; Xiaorong Sun; Lu Sun; Peter G Maxim; Lei Xing; Yong Huang; Wenwu Li; Honglin Wan; Xianguang Zhao; Ligang Xing; Jinming Yu
Journal:  PLoS One       Date:  2016-06-20       Impact factor: 3.240

8.  Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis.

Authors:  Jiangdian Song; Zaiyi Liu; Wenzhao Zhong; Yanqi Huang; Zelan Ma; Di Dong; Changhong Liang; Jie Tian
Journal:  Sci Rep       Date:  2016-12-06       Impact factor: 4.379

Review 9.  Development and clinical application of radiomics in lung cancer.

Authors:  Bojiang Chen; Rui Zhang; Yuncui Gan; Lan Yang; Weimin Li
Journal:  Radiat Oncol       Date:  2017-09-15       Impact factor: 3.481

10.  Quantitative CT variables enabling response prediction in neoadjuvant therapy with EGFR-TKIs: are they different from those in neoadjuvant concurrent chemoradiotherapy?

Authors:  Yousun Chong; Jae-Hun Kim; Ho Yun Lee; Yong Chan Ahn; Kyung Soo Lee; Myung-Ju Ahn; Jhingook Kim; Young Mog Shim; Joungho Han; Yoon-La Choi
Journal:  PLoS One       Date:  2014-02-26       Impact factor: 3.240
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