Literature DB >> 26934729

Exploration of Imaging Biomarkers for Predicting Survival of Patients With Advanced Non-Small Cell Lung Cancer Treated With Antiangiogenic Chemotherapy.

Koichi Hayano1, Naveen M Kulkarni1, Dan G Duda2, Rebecca Suk Heist3, Dushyant V Sahani1.   

Abstract

OBJECTIVE: The objective of this study was to compare imaging biomarkers, including (18)F-FDG PET, CT perfusion (CTP), and CT texture analysis (CTTA), in predicting the survival of patients with advanced non-small cell lung cancer (NSCLC) treated with antiangiogenic chemotherapy. SUBJECTS AND METHODS: A total of 35 patients (17 men and 18 women; median age, 64.0 years) with advanced NSCLC treated with antiangiogenic chemotherapy were evaluated. CTP and FDG PET were performed before the therapy, and blood flow, blood volume, mean transit time, and the maximum standardized uptake value (SUV max) of the tumor were measured. Texture parameters, including the mean value of pixels with positive values (MPP) and entropy (a measure of irregularity), were also measured on pretherapeutic unenhanced CT images, using CTTA software with a medium texture scale filtration. The best percent change in the tumor burden was also measured. These image-derived tumor parameters were then compared with progression-free survival (PFS) and overall survival (OS).
RESULTS: In univariate Cox regression analysis, MPP and entropy were significantly correlated with PFS (p = 0.01 and p = 0.01, respectively), whereas SUV max, MPP, and entropy were significantly correlated with OS (p = 0.03, p = 0.04, and p = 0.0008, respectively). In Kaplan-Meier analysis, high MPP and low entropy were significantly associated with favorable PFS (p < 0.0001 and p = 0.03, respectively) and OS (p = 0.0009 and p = 0.005, respectively), and low SUV max was significantly associated with favorable OS (p = 0.01). CTP parameters and the best change in the tumor burden had no associations with survival. In multivariate analysis, only entropy was identified as an independent prognostic factor for OS (p = 0.02).
CONCLUSION: CTTA is the optimal imaging biomarker for predicting the survival of patients with advanced NSCLC treated with antiangiogenic chemotherapy.

Entities:  

Keywords:  CT perfusion; PET; antiangiogenic therapy; non–small cell lung cancer; texture analysis

Mesh:

Substances:

Year:  2016        PMID: 26934729     DOI: 10.2214/AJR.15.15528

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  12 in total

1.  Texture features of colorectal liver metastases on pretreatment contrast-enhanced CT may predict response and prognosis in patients treated with bevacizumab-containing chemotherapy: a pilot study including comparison with standard chemotherapy.

Authors:  Marco Ravanelli; Giorgio Maria Agazzi; Elena Tononcelli; Elisa Roca; Paolo Cabassa; Gianluca Baiocchi; Alfredo Berruti; Roberto Maroldi; Davide Farina
Journal:  Radiol Med       Date:  2019-06-06       Impact factor: 3.469

2.  Prognostic value of radiomic analysis of iodine overlay maps from dual-energy computed tomography in patients with resectable lung cancer.

Authors:  Jooae Choe; Sang Min Lee; Kyung-Hyun Do; Jung Bok Lee; Sang Min Lee; June-Goo Lee; Joon Beom Seo
Journal:  Eur Radiol       Date:  2018-07-27       Impact factor: 5.315

3.  Predicting Lung Cancer Patients' Survival Time via Logistic Regression-based Models in a Quantitative Radiomic Framework.

Authors:  Shayesteh S P; Shiri I; Karami A H; Hashemian R; Kooranifar S; Ghaznavi H; Shakeri-Zadeh A
Journal:  J Biomed Phys Eng       Date:  2020-08-01

Review 4.  Clinical applications of textural analysis in non-small cell lung cancer.

Authors:  Iain Phillips; Mazhar Ajaz; Veni Ezhil; Vineet Prakash; Sheaka Alobaidli; Sarah J McQuaid; Christopher South; James Scuffham; Andrew Nisbet; Philip Evans
Journal:  Br J Radiol       Date:  2017-10-27       Impact factor: 3.039

Review 5.  Imaging of Precision Therapy for Lung Cancer: Current State of the Art.

Authors:  Hyesun Park; Lynette M Sholl; Hiroto Hatabu; Mark M Awad; Mizuki Nishino
Journal:  Radiology       Date:  2019-08-06       Impact factor: 11.105

6.  Whole-tumor perfusion CT using texture analysis in unresectable stage IIIA/B non-small cell lung cancer treated with recombinant human endostatin.

Authors:  Lei Shi; Xiang-Lan Zhou; Jing-Jing Sun; Jie-Hui Huang; Xu Wang; Kai Li; Pei-Pei Pang; Yu-Jin Xu; Ming Chen; Min-Ming Zhang
Journal:  Quant Imaging Med Surg       Date:  2019-06

7.  Predicting survival time of lung cancer patients using radiomic analysis.

Authors:  Ahmad Chaddad; Christian Desrosiers; Matthew Toews; Bassam Abdulkarim
Journal:  Oncotarget       Date:  2017-11-01

8.  Multislice Analysis of Blood Flow Values in CT Perfusion Studies of Lung Cancer.

Authors:  Silvia Malavasi; Domenico Barone; Giampaolo Gavelli; Alessandro Bevilacqua
Journal:  Biomed Res Int       Date:  2017-01-10       Impact factor: 3.411

9.  The effects of segmentation algorithms on the measurement of 18F-FDG PET texture parameters in non-small cell lung cancer.

Authors:  Usman Bashir; Gurdip Azad; Muhammad Musib Siddique; Saana Dhillon; Nikheel Patel; Paul Bassett; David Landau; Vicky Goh; Gary Cook
Journal:  EJNMMI Res       Date:  2017-07-26       Impact factor: 3.138

10.  Quantification of Structural Heterogeneity Using Fractal Analysis of Contrast-Enhanced CT Image to Predict Survival in Gastric Cancer Patients.

Authors:  Hiroki Watanabe; Koichi Hayano; Gaku Ohira; Shunsuke Imanishi; Toshiharu Hanaoka; Atsushi Hirata; Masayuki Kano; Hisahiro Matsubara
Journal:  Dig Dis Sci       Date:  2020-07-20       Impact factor: 3.199

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