Literature DB >> 26176655

Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors.

David V Fried1, Osama Mawlawi1, Lifei Zhang1, Xenia Fave1, Shouhao Zhou1, Geoffrey Ibbott1, Zhongxing Liao1, Laurence E Court1.   

Abstract

PURPOSE: To determine whether quantitative imaging features from pretreatment positron emission tomography (PET) can enhance patient overall survival risk stratification beyond what can be achieved with conventional prognostic factors in patients with stage III non-small cell lung cancer (NSCLC).
MATERIALS AND METHODS: The institutional review board approved this retrospective chart review study and waived the requirement to obtain informed consent. The authors retrospectively identified 195 patients with stage III NSCLC treated definitively with radiation therapy between January 2008 and January 2013. All patients underwent pretreatment PET/computed tomography before treatment. Conventional PET metrics, along with histogram, shape and volume, and co-occurrence matrix features, were extracted. Linear predictors of overall survival were developed from leave-one-out cross-validation. Predictive Kaplan-Meier curves were used to compare the linear predictors with both quantitative imaging features and conventional prognostic factors to those generated with conventional prognostic factors alone. The Harrell concordance index was used to quantify the discriminatory power of the linear predictors for survival differences of at least 0, 6, 12, 18, and 24 months. Models were generated with features present in more than 50% of the cross-validation folds.
RESULTS: Linear predictors of overall survival generated with both quantitative imaging features and conventional prognostic factors demonstrated improved risk stratification compared with those generated with conventional prognostic factors alone in terms of log-rank statistic (P = .18 vs P = .0001, respectively) and concordance index (0.62 vs 0.58, respectively). The use of quantitative imaging features selected during cross-validation improved the model using conventional prognostic factors alone (P = .007). Disease solidity and primary tumor energy from the co-occurrence matrix were found to be selected in all folds of cross-validation.
CONCLUSION: Pretreatment PET features were associated with overall survival when adjusting for conventional prognostic factors in patients with stage III NSCLC. © RSNA, 2015.

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Year:  2015        PMID: 26176655      PMCID: PMC4699494          DOI: 10.1148/radiol.2015142920

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  23 in total

1.  Non-small cell lung cancer: histopathologic correlates for texture parameters at CT.

Authors:  Balaji Ganeshan; Vicky Goh; Henry C Mandeville; Quan Sing Ng; Peter J Hoskin; Kenneth A Miles
Journal:  Radiology       Date:  2012-11-20       Impact factor: 11.105

2.  Intratumoral Metabolic Heterogeneity for Prediction of Disease Progression After Concurrent Chemoradiotherapy in Patients with Inoperable Stage III Non-Small-Cell Lung Cancer.

Authors:  Sae-Ryung Kang; Ho-Chun Song; Byung Hyun Byun; Jong-Ryool Oh; Hyeon-Sik Kim; Sun-Pyo Hong; Seong Young Kwon; Ari Chong; Jahae Kim; Sang-Geon Cho; Hee Jeong Park; Young-Chul Kim; Sung-Ja Ahn; Jung-Joon Min; Hee-Seung Bom
Journal:  Nucl Med Mol Imaging       Date:  2013-09-06

3.  Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters.

Authors:  Paulina E Galavis; Christian Hollensen; Ngoneh Jallow; Bhudatt Paliwal; Robert Jeraj
Journal:  Acta Oncol       Date:  2010-10       Impact factor: 4.089

4.  Prognostic factors in stage III non-small cell lung cancer: a review of conventional, metabolic and new biological variables.

Authors:  Thierry Berghmans; Marianne Paesmans; Jean-Paul Sculier
Journal:  Ther Adv Med Oncol       Date:  2011-05       Impact factor: 8.168

5.  Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation.

Authors:  Mathieu Hatt; Catherine Cheze-le Rest; Angela van Baardwijk; Philippe Lambin; Olivier Pradier; Dimitris Visvikis
Journal:  J Nucl Med       Date:  2011-10-11       Impact factor: 10.057

6.  Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability.

Authors:  Ralph T H Leijenaar; Sara Carvalho; Emmanuel Rios Velazquez; Wouter J C van Elmpt; Chintan Parmar; Otto S Hoekstra; Corneline J Hoekstra; Ronald Boellaard; André L A J Dekker; Robert J Gillies; Hugo J W L Aerts; Philippe Lambin
Journal:  Acta Oncol       Date:  2013-09-09       Impact factor: 4.089

Review 7.  Predictive and prognostic value of FDG-PET in nonsmall-cell lung cancer: a systematic review.

Authors:  Lioe-Fee de Geus-Oei; Henricus F M van der Heijden; Frans H M Corstens; Wim J G Oyen
Journal:  Cancer       Date:  2007-10-15       Impact factor: 6.860

8.  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

Review 9.  From RECIST to PERCIST: Evolving Considerations for PET response criteria in solid tumors.

Authors:  Richard L Wahl; Heather Jacene; Yvette Kasamon; Martin A Lodge
Journal:  J Nucl Med       Date:  2009-05       Impact factor: 10.057

10.  Prognostic value of volumetric parameters of (18)F-FDG PET in non-small-cell lung cancer: a meta-analysis.

Authors:  Hyung-Jun Im; Kyoungjune Pak; Gi Jeong Cheon; Keon Wook Kang; Seong-Jang Kim; In-Joo Kim; June-Key Chung; E Edmund Kim; Dong Soo Lee
Journal:  Eur J Nucl Med Mol Imaging       Date:  2014-09-06       Impact factor: 9.236

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  38 in total

1.  Pretreatment 18F-FDG PET Textural Features in Locally Advanced Non-Small Cell Lung Cancer: Secondary Analysis of ACRIN 6668/RTOG 0235.

Authors:  Nitin Ohri; Fenghai Duan; Bradley S Snyder; Bo Wei; Mitchell Machtay; Abass Alavi; Barry A Siegel; Douglas W Johnson; Jeffrey D Bradley; Albert DeNittis; Maria Werner-Wasik; Issam El Naqa
Journal:  J Nucl Med       Date:  2016-02-11       Impact factor: 10.057

2.  Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis.

Authors:  Rachel B Ger; Daniel F Craft; Dennis S Mackin; Shouhao Zhou; Rick R Layman; A Kyle Jones; Hesham Elhalawani; Clifton D Fuller; Rebecca M Howell; Heng Li; R Jason Stafford; Laurence E Court
Journal:  Comput Med Imaging Graph       Date:  2018-09-15       Impact factor: 4.790

Review 3.  Radiomics in Oncological PET/CT: Clinical Applications.

Authors:  Jeong Won Lee; Sang Mi Lee
Journal:  Nucl Med Mol Imaging       Date:  2017-10-20

Review 4.  Characterization of PET/CT images using texture analysis: the past, the present… any future?

Authors:  Mathieu Hatt; Florent Tixier; Larry Pierce; Paul E Kinahan; Catherine Cheze Le Rest; Dimitris Visvikis
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-06-06       Impact factor: 9.236

5.  18F-FDG PET/CT heterogeneity quantification through textural features in the era of harmonisation programs: a focus on lung cancer.

Authors:  Charline Lasnon; Mohamed Majdoub; Brice Lavigne; Pascal Do; Jeannick Madelaine; Dimitris Visvikis; Mathieu Hatt; Nicolas Aide
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-06-21       Impact factor: 9.236

6.  Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics.

Authors:  Rachel B Ger; Carlos E Cardenas; Brian M Anderson; Jinzhong Yang; Dennis S Mackin; Lifei Zhang; Laurence E Court
Journal:  J Vis Exp       Date:  2018-01-08       Impact factor: 1.355

Review 7.  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

8.  The emerging field of radiomics in esophageal cancer: current evidence and future potential.

Authors:  Peter S N van Rossum; Cai Xu; David V Fried; Lucas Goense; Laurence E Court; Steven H Lin
Journal:  Transl Cancer Res       Date:  2016-08       Impact factor: 1.241

9.  Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?

Authors:  Xenia Fave; Dennis Mackin; Jinzhong Yang; Joy Zhang; David Fried; Peter Balter; David Followill; Daniel Gomez; A Kyle Jones; Francesco Stingo; Jonas Fontenot; Laurence Court
Journal:  Med Phys       Date:  2015-12       Impact factor: 4.071

10.  Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis.

Authors:  Jia Wu; Todd Aguilera; David Shultz; Madhu Gudur; Daniel L Rubin; Billy W Loo; Maximilian Diehn; Ruijiang Li
Journal:  Radiology       Date:  2016-04-05       Impact factor: 11.105

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