Literature DB >> 30527295

CT texture analysis as predictive factor in metastatic lung adenocarcinoma treated with tyrosine kinase inhibitors (TKIs).

Marco Ravanelli1, Giorgio M Agazzi1, Balaji Ganeshan2, Elisa Roca3, Elena Tononcelli4, Valeria Bettoni1, Alberto Caprioli5, Andrea Borghesi1, Alfredo Berruti3, Roberto Maroldi1, Davide Farina1.   

Abstract

PURPOSE: To assess the predictive and prognostic value of pre-treatment CT texture features in lung adenocarcinoma treated with tyrosine kinase inhibitors (TKI).
MATERIALS AND METHODS: Texture analysis was performed using commercially available software (TexRAD Ltd, Cambridge, UK) on pre-treatment contrast-enhanced CT studies from 50 patients with metastatic lung adenocarcinoma treated by TKI. Texture features were quantified on a 5-mm-thick central slice of the primary tumor and were correlated with progression-free and overall survival (PFS and OS) using an internally cross-validated machine learning approach then validated on a bootstrapped sample.
RESULTS: Median PFS and OS were 10.5 and 20.7 months, respectively. A noninvasive signature based on five texture parameters predicted 6-month progression with Area Under the Curve (AUC) of 0.8 (95% CI) and 1-year progression with AUC of 0.76. A high-risk group had hazard ratios for progression of 4.63 and 5.78 when divided by median and best cut-off points, respectively. Texture signature did not correlate with OS. Available clinical variables did not correlate with PFS or with OS.
CONCLUSION: Texture features seem to be associated with PFS in lung adenocarcinoma treated with TKI.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Adenocarcinoma; Carcinoma; Epidermal growth factor; Non small cell lung; Prognosis; Progression-free survival; Protein kinase inhibitors; Receptor; Texture analysis; Tomography; X-ray computed

Mesh:

Substances:

Year:  2018        PMID: 30527295     DOI: 10.1016/j.ejrad.2018.10.016

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  11 in total

1.  CT texture analysis for prediction of EGFR mutational status and ALK rearrangement in patients with non-small cell lung cancer.

Authors:  Giorgio Maria Agazzi; Marco Ravanelli; Elisa Roca; Daniela Medicina; Piera Balzarini; Carlotta Pessina; William Vermi; Alfredo Berruti; Roberto Maroldi; Davide Farina
Journal:  Radiol Med       Date:  2021-01-29       Impact factor: 3.469

2.  Computed tomography radiomic features hold prognostic utility for canine lung tumors: An analytical study.

Authors:  Hannah Able; Amber Wolf-Ringwall; Aaron Rendahl; Christopher P Ober; Davis M Seelig; Chris T Wilke; Jessica Lawrence
Journal:  PLoS One       Date:  2021-08-17       Impact factor: 3.240

3.  Artificial Intelligence in Pharmacoepidemiology: A Systematic Review. Part 1-Overview of Knowledge Discovery Techniques in Artificial Intelligence.

Authors:  Maurizio Sessa; Abdul Rauf Khan; David Liang; Morten Andersen; Murat Kulahci
Journal:  Front Pharmacol       Date:  2020-07-16       Impact factor: 5.810

4.  CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy.

Authors:  Fengchang Yang; Jiayi Zhang; Liu Zhou; Wei Xia; Rui Zhang; Haifeng Wei; Jinxue Feng; Xingyu Zhao; Junming Jian; Xin Gao; Shuanghu Yuan
Journal:  Eur Radiol       Date:  2021-09-26       Impact factor: 7.034

5.  Three dimensional texture analysis of noncontrast chest CT in differentiating solitary solid lung squamous cell carcinoma from adenocarcinoma and correlation to immunohistochemical markers.

Authors:  Rui Han; Roshan Arjal; Jin Dong; Hong Jiang; Huan Liu; Dongyou Zhang; Lu Huang
Journal:  Thorac Cancer       Date:  2020-09-18       Impact factor: 3.500

6.  Uronic acid metabolic process-related gene expression-based signature predicts overall survival of glioma.

Authors:  Yuemei Feng; Guanzhang Li; Zhongfang Shi; Xu Yan; Renpeng Li; You Zhai; Yuanhao Chang; Di Wang; Ulf Dietrich Kahlert; Wei Zhang; Fang Yuan
Journal:  Biosci Rep       Date:  2021-01-29       Impact factor: 3.840

7.  Machine Learning-Based CT Radiomics Analysis for Prognostic Prediction in Metastatic Non-Small Cell Lung Cancer Patients With EGFR-T790M Mutation Receiving Third-Generation EGFR-TKI Osimertinib Treatment.

Authors:  Xin Tang; Yuan Li; Wei-Feng Yan; Wen-Lei Qian; Tong Pang; You-Ling Gong; Zhi-Gang Yang
Journal:  Front Oncol       Date:  2021-09-29       Impact factor: 6.244

8.  Texture Analysis of Fractional Water Content Images Acquired during PET/MRI: Initial Evidence for an Association with Total Lesion Glycolysis, Survival and Gene Mutation Profile in Primary Colorectal Cancer.

Authors:  Balaji Ganeshan; Kenneth Miles; Asim Afaq; Shonit Punwani; Manuel Rodriguez; Simon Wan; Darren Walls; Luke Hoy; Saif Khan; Raymond Endozo; Robert Shortman; John Hoath; Aman Bhargava; Matthew Hanson; Daren Francis; Tan Arulampalam; Sanjay Dindyal; Shih-Hsin Chen; Tony Ng; Ashley Groves
Journal:  Cancers (Basel)       Date:  2021-05-31       Impact factor: 6.639

Review 9.  What's New on Quantitative CT Analysis as a Tool to Predict Growth in Persistent Pulmonary Subsolid Nodules? A Literature Review.

Authors:  Andrea Borghesi; Silvia Michelini; Salvatore Golemi; Alessandra Scrimieri; Roberto Maroldi
Journal:  Diagnostics (Basel)       Date:  2020-01-21

10.  Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia.

Authors:  Damiano Caruso; Francesco Pucciarelli; Marta Zerunian; Balaji Ganeshan; Domenico De Santis; Michela Polici; Carlotta Rucci; Tiziano Polidori; Gisella Guido; Benedetta Bracci; Antonella Benvenga; Luca Barbato; Andrea Laghi
Journal:  Radiol Med       Date:  2021-08-04       Impact factor: 3.469

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.