Literature DB >> 29312866

Quantitative texture analysis on pre-treatment computed tomography predicts local recurrence in stage I non-small cell lung cancer following stereotactic radiation therapy.

Carole Dennie1, Rebecca Thornhill1, Carolina A Souza1, Cecilia Odonkor2, Jason R Pantarotto3, Robert MacRae3, Graham Cook3.   

Abstract

BACKGROUND: The prediction of local recurrence (LR) of stage I non-small cell lung cancer (NSCLC) after definitive stereotactic body radiotherapy (SBRT) remains elusive. The purpose of this study was to assess whether quantitative imaging features on pre-treatment computed tomography (CT) can predict LR beyond 18 (18F) fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/CT maximum standard uptake value (SUVmax).
METHODS: This retrospective study evaluated 36 patients with 37 stage I NSCLC who had local tumor control (LC; n=19) and (LR; n=18). Textural features were extracted on pre-treatment CT. Mann-Whitney U tests were used to compare LC and LR groups. Receiver-operating characteristic (ROC) curves were constructed and the area under the curve (AUC) calculated with LR as outcome.
RESULTS: Gray-level correlation and sum variance were greater in the LR group, compared with the LC group (P=0.02 and P=0.04, respectively). Gray-level difference variance was lower in the LR group (P=0.004). The logistic regression model generated using gray-level correlation and difference variance features resulted in AUC (SE) 0.77 (0.08) (P=0.0007). The addition of 18F-FDG PET/CT SUVmax did not improve the AUC (P=0.75).
CONCLUSIONS: CT textural features were found to be predictors of LR of early stage NSCLC on baseline CT prior to SBRT.

Entities:  

Keywords:  Computed tomography imaging (CT imaging); local recurrence (LR); non-small cell lung cancer (NSCLC); positron emission tomography (PET)/CT imaging; stereotactic radiotherapy; texture analysis

Year:  2017        PMID: 29312866      PMCID: PMC5756784          DOI: 10.21037/qims.2017.11.01

Source DB:  PubMed          Journal:  Quant Imaging Med Surg        ISSN: 2223-4306


  24 in total

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2.  Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

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Review 7.  Imaging Heterogeneity in Lung Cancer: Techniques, Applications, and Challenges.

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Authors:  Gary J R Cook; Connie Yip; Muhammad Siddique; Vicky Goh; Sugama Chicklore; Arunabha Roy; Paul Marsden; Shahreen Ahmad; David Landau
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10.  FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy.

Authors:  Pierre Lovinfosse; Zsolt Levente Janvary; Philippe Coucke; Sébastien Jodogne; Claire Bernard; Mathieu Hatt; Dimitris Visvikis; Nicolas Jansen; Bernard Duysinx; Roland Hustinx
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-01-30       Impact factor: 9.236

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2.  Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening.

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4.  Prognostic Role Of Computed Tomography Textural Features In Early-Stage Non-Small Cell Lung Cancer Patients Receiving Stereotactic Body Radiotherapy.

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Journal:  Cancer Manag Res       Date:  2019-11-25       Impact factor: 3.989

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

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7.  A Novel Radiomics Nomogram for the Prediction of Secondary Loss of Response to Infliximab in Crohn's Disease.

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