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A systematic review and meta-analysis of the prognostic value of radiomics based models in non-small cell lung cancer treated with curative radiotherapy

  • Gargi Kothari
    Correspondence
    Corresponding author at: Peter MacCallum Cancer Centre, Victorian Comprehensive Cancer Centre Building, 305 Grattan Street, Melbourne, Victoria 3000, Australia.
    Affiliations
    Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia

    Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia
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  • James Korte
    Affiliations
    Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia

    Department of Biomedical Engineering, School of Engineering, University of Melbourne, Melbourne, Australia
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  • Eric J. Lehrer
    Affiliations
    Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, USA
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  • Nicholas G. Zaorsky
    Affiliations
    Department of Radiation Oncology, Penn State Cancer Institute, Hershey, USA

    Department of Public Health Sciences, Penn State College of Medicine, Hershey, USA
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  • Smaro Lazarakis
    Affiliations
    Health Sciences Library, Peter MacCallum Cancer Centre, Parkville, Australia
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  • Tomas Kron
    Affiliations
    Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia

    Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia

    Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
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  • Nicholas Hardcastle
    Affiliations
    Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia

    Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
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  • Shankar Siva
    Affiliations
    Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia

    Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia
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Published:October 20, 2020DOI:https://doi.org/10.1016/j.radonc.2020.10.023

      Highlights

      • This is a systematic review of radiomics based prognostic models in lung cancer.
      • It includes 40 studies of 55 datasets and 6223 patients.
      • There was heterogeneity in methodology and features included in prognostic models.
      • Meta-analysis found a C-index random effects estimate of 0.57 (95%CI 0.53 – 0.62).
      • Standard features and robust selection techniques should be used in future studies.

      Abstract

      Background and purpose

      Radiomics allows extraction of quantifiable features from imaging. This study performs a systematic review and meta-analysis of the performance of radiomics based prognostic models in non-small cell lung cancer (NSCLC).

      Materials and methods

      A literature review was performed following PRISMA guidelines. Medline, EMBASE and Cochrane databases were searched for articles investigating radiomics features predictive of overall survival (OS) in NSCLC treated with curative intent radiotherapy. A random-effects meta-analysis of Harrell’s Concordance Index (C-index) was performed on the performance of radiomics models.

      Results

      Of the 2746 articles retrieved, 40 studies of 55 datasets and 6223 patients were eligible for inclusion in the systematic review. There was significant heterogeneity in the methodology for feature selection and model development. Twelve datasets reported the C-index of radiomics based models in predicting OS and were included in the meta-analysis. The C-index random effects estimate was 0.57 (95% CI 0.53–0.62). There was significant heterogeneity (I2 = 70.3%).

      Conclusions

      Based on this review, radiomics based models for lung cancer have to date demonstrated modest prognostic capabilities. Future research should consider using standardised radiomics features, robust feature selection and model development, and deep learning techniques, absolving the need for pre-defined features, to improve imaging-based models.

      Keywords

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      References

        • Siegel R.L.
        • Miller K.D.
        • Jemal A.
        Cancer statistics, 2019.
        CA: Cancer J Clin. 2019; 69: 7-34
        • Detterbeck F.C.
        • Boffa D.J.
        • Kim A.W.
        • Tanoue L.T.
        The eighth edition lung cancer stage classification.
        Chest. 2017; 151: 193-203
        • Alexander M.
        • Wolfe R.
        • Ball D.
        • et al.
        Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer.
        Br J Cancer. 2017; 117: 744-751
        • Mahar A.L.
        • Compton C.
        • McShane L.M.
        • et al.
        Refining prognosis in lung cancer: a report on the quality and relevance of clinical prognostic tools.
        J Thorac Oncol. 2015; 10: 1576-1589
        • Aerts H.J.
        • Velazquez E.R.
        • Leijenaar R.T.
        • et al.
        Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
        Nat Commun. 2014; 5: 4006
        • Lambin P.
        • Rios-Velazquez E.
        • Leijenaar R.
        • et al.
        Radiomics: extracting more information from medical images using advanced feature analysis.
        Eur J Cancer. 2012; 48: 441-446https://doi.org/10.1016/j.ejca.2011.11.036
        • Tu W.
        • Sun G.
        • Fan L.
        • et al.
        Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology.
        Lung Cancer. 2019; 132: 28-35https://doi.org/10.1016/j.lungcan.2019.03.025
        • Linning E.
        • Lu L.
        • Li L.
        • Yang H.
        • Schwartz L.H.
        • Zhao B.
        Radiomics for classifying histological subtypes of lung cancer based on multiphasic contrast-enhanced computed tomography.
        J Comput Assist Tomogr. 2019; 43: 300-306
        • Hawkins S.
        • Wang H.
        • Liu Y.
        • et al.
        Predicting malignant nodules from screening CT scans.
        J Thorac Oncol. 2016; 11: 2120-2128
        • Wu W.
        • Parmar C.
        • Grossmann P.
        • et al.
        Exploratory study to identify radiomics classifiers for lung cancer histology.
        Front Oncol. 2016; 6: 2016
        • Bianconi F.
        • Palumbo I.
        • Fravolini M.L.
        • et al.
        Texture Analysis on [18 F] FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types.
        Mol Imag Biol. 2019; 21: 1200-1209
        • Lafata K.
        • Cai J.
        • Wang C.
        • Hong J.
        • Kelsey C.R.
        • Yin F.-F.
        Spatial-temporal variability of radiomic features and its effect on the classification of lung cancer histology.
        Phys Med Biol. 2018; 63225003
        • Zhu X.
        • Dong D.
        • Chen Z.
        • et al.
        Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer.
        Eur Radiol. 2018; 28: 2772-2778
        • Brunese L.
        • Greco B.
        • Setola F.R.
        • et al.
        Non-small cell lung cancer evaluated with quantitative contrast-enhanced CT and PET-CT: net enhancement and standardized uptake values are related to tumour size and histology.
        Med Sci Monitor. 2013; 19: 95
        • Lafata K.J.
        • Hong J.C.
        • Geng R.
        • et al.
        Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy.
        Phys Med Biol. 2019; 64025007
        • Huang Y.
        • Liu Z.
        • He L.
        • et al.
        Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non—small cell lung cancer.
        Radiology. 2016; 281: 947-957
        • Huynh E.
        • Coroller T.P.
        • Narayan V.
        • et al.
        CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer.
        Radiother Oncol. 2016; 120: 258-266
        • Mayr A.
        • Schmid M.
        Boosting the concordance index for survival data–a unified framework to derive and evaluate biomarker combinations.
        PLoS ONE. 2014; 9e84483https://doi.org/10.1371/journal.pone.0084483
        • Collins G.S.
        • Reitsma J.B.
        • Altman D.G.
        • Moons K.G.
        Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) The TRIPOD Statement.
        Circulation. 2015; 131: 211-219
      1. Team RC. R: A language and environment for statistical computing [Internet]. Vienna (Austria): R Foundation for Statistical Computing [cited 2019 Aug 8]. 2020.

        • Viechtbauer W.
        Conducting meta-analyses in R with the metafor package.
        J Stat Softw. 2010; 36: 1-48
        • Balduzzi S.
        • Rücker G.
        • Schwarzer G.
        How to perform a meta-analysis with R: a practical tutorial.
        Evid-Based Mental Health. 2019; 22: 153-160
        • Aerts H.J.
        • Velazquez E.R.
        • Leijenaar R.T.
        • et al.
        Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. [Erratum appears in Nat Commun. 2014;5:4644 Note: Cavalho, Sara [corrected to Carvalho, Sara]].
        Nat Commun. 2014; 5: 4006
        • Li Q.
        • Kim J.
        • Balagurunathan Y.
        • et al.
        Imaging features from pretreatment CT scans are associated with clinical outcomes in nonsmall-cell lung cancer patients treated with stereotactic body radiotherapy.
        Med Phys. 2017; 44: 4341-4349
        • Ohri N.
        • Duan F.
        • Snyder B.S.
        • et al.
        Pretreatment 18F-FDG PET textural features in locally advanced non-small cell lung cancer: secondary analysis of ACRIN 6668/RTOG 0235.
        J Nucl Med. 2016; 57: 842-848
        • Fried D.V.
        • Mawlawi O.
        • Zhang L.
        • et al.
        Stage III non-small cell lung cancer: prognostic value of FDG PET quantitative imaging features combined with clinical prognostic factors.
        Radiology. 2016; 278: 214-222
        • Li H.
        • Galperin-Aizenberg M.
        • Pryma D.
        • Simone C.B.
        • Fan Y.
        Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy.
        Radiother Oncol. 2018; 129: 218-226
        • van Timmeren J.E.
        • Carvalho S.
        • Leijenaar R.T.H.
        • et al.
        Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics.
        PLoS ONE. 2019; 14e0217536https://doi.org/10.1371/journal.pone.0217536
        • Li Q.
        • Kim J.
        • Balagurunathan Y.
        • et al.
        CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy.
        Radiat Oncol. 2017; 12: 158
        • Wang L.
        • Dong T.
        • Xin B.
        • et al.
        Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer.
        Eur Radiol. 2019; 29: 2958-2967https://doi.org/10.1007/s00330-018-5949-2
        • Sun W.
        • Jiang M.
        • Dang J.
        • Chang P.
        • Yin F.F.
        Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis.
        Radiat Oncol. 2018; 13: 197
        • Parmar C.
        • Grossmann P.
        • Bussink J.
        • Lambin P.
        • Aerts H.J.
        Machine learning methods for quantitative radiomic biomarkers.
        Sci Rep. 2015; 5: 13087
        • Zhang Y.
        • Oikonomou A.
        • Wong A.
        • Haider M.A.
        • Khalvati F.
        Radiomics-based prognosis analysis for non-small cell lung cancer.
        Sci Rep. 2017; 7: 46349
        • Fave X.
        • Zhang L.
        • Yang J.
        • et al.
        Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer.
        Transl Cancer Res. 2016; 5: 349-363
        • Yip S.S.F.
        • Liu Y.
        • Parmar C.
        • et al.
        Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer.
        Sci Rep. 2017; 7: 3519https://doi.org/10.1038/s41598-017-02425-5
        • Ganeshan B.
        • Goh V.
        • Mandeville H.C.
        • Ng Q.S.
        • Hoskin P.J.
        • Miles K.A.
        Non–small cell lung cancer: histopathologic correlates for texture parameters at CT.
        Radiology. 2013; 266: 326-336
        • Tang C.
        • Hobbs B.
        • Amer A.
        • et al.
        Development of an immune-pathology informed radiomics model for non-small cell lung cancer.
        Sci Rep. 2018; 8: 1-9
      2. Digumarthy SR, Padole AM, Gullo RL, Sequist LV, Kalra MK. Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status? Medicine. 2019;98(1):e13963-e. doi:10.1097/MD.0000000000013963.

        • Lu L.
        • Sun S.H.
        • Yang H.
        • et al.
        Radiomics prediction of EGFR status in lung cancer-our experience in using multiple feature extractors and the cancer imaging archive data.
        Tomography. 2020; 6: 223-230https://doi.org/10.18383/j.tom.2020.00017
        • Foy J.J.
        • Robinson K.R.
        • Li H.
        • Giger M.L.
        • Al-Hallaq H.
        • Armato S.G.
        Variation in algorithm implementation across radiomics software.
        J Med Imaging. 2018; 5044505
        • Chang Y.
        • Lafata K.
        • Wang C.
        • et al.
        Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes.
        Biomed Phys Eng Express. 2020; 6025016
        • Zwanenburg A.
        • Vallières M.
        • Abdalah M.A.
        • et al.
        The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping.
        Radiology. 2020; 295: 328-338
        • Shi Z.
        • Traverso A.
        • van Soest J.
        • Dekker A.
        • Wee L.
        Technical Note: Ontology-guided radiomics analysis workflow (O-RAW).
        Med Phys. 2019; 46: 5677-5684https://doi.org/10.1002/mp.13844
        • Shi Z.
        • Zhovannik I.
        • Traverso A.
        • et al.
        Distributed radiomics as a signature validation study using the Personal Health Train infrastructure.
        Sci Data. 2019; 6: 218https://doi.org/10.1038/s41597-019-0241-0
        • van Timmeren J.E.
        • Leijenaar R.T.H.
        • van Elmpt W.
        • et al.
        Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam CT images.
        Radiother Oncol. 2017; 123: 363-369
        • Zwanenburg A.
        Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis.
        Eur J Nucl Med Mol Imaging. 2019; 46: 2638-2655https://doi.org/10.1007/s00259-019-04391-8
        • Zwanenburg A.
        • Löck S.
        Why validation of prognostic models matters?.
        Radiother Oncol. 2018; 127: 370-373https://doi.org/10.1016/j.radonc.2018.03.004
        • LeCun Y.
        • Bengio Y.
        • Hinton G.
        Deep learning.
        Nature. 2015; 521: 436-444https://doi.org/10.1038/nature14539
        • Ardila D.
        • Kiraly A.P.
        • Bharadwaj S.
        • et al.
        End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.
        Nat Med. 2019; 25: 954-961https://doi.org/10.1038/s41591-019-0447-x
        • Hua K.-L.
        • Hsu C.-H.
        • Hidayati S.C.
        • Cheng W.-H.
        • Chen Y.-J.
        Computer-aided classification of lung nodules on computed tomography images via deep learning technique.
        OncoTargets Ther. 2015; 8
        • Wang S.
        • Shi J.
        • Ye Z.
        • et al.
        Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.
        Eur Respir J. 2019; 53
        • Lustberg T.
        • van Soest J.
        • Gooding M.
        • et al.
        Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer.
        Radiother Oncol. 2018; 126: 312-317
        • Hosny A.
        • Parmar C.
        • Coroller T.P.
        • et al.
        Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.
        PLoS Med. 2018; 15e1002711https://doi.org/10.1371/journal.pmed.1002711
        • Xu Y.
        • Hosny A.
        • Zeleznik R.
        • et al.
        Deep learning predicts lung cancer treatment response from serial medical imaging.
        Clin Cancer Res. 2019; 25: 3266-3275https://doi.org/10.1158/1078-0432.CCR-18-2495
        • Chaddad A.
        • Desrosiers C.
        • Toews M.
        • Abdulkarim B.
        Predicting survival time of lung cancer patients using radiomic analysis.
        Oncotarget. 2017; 8: 104393-104407
        • Bianconi F.
        • Fravolini M.L.
        • Bello-Cerezo R.
        • Minestrini M.
        • Scialpi M.
        • Palumbo B.
        Evaluation of shape and textural features from CT as prognostic biomarkers in non-small cell lung cancer.
        Anticancer Res. 2018; 38: 2155-2160
        • Soufi M.
        • Arimura H.
        • Nakamoto T.
        • et al.
        Exploration of temporal stability and prognostic power of radiomic features based on electronic portal imaging device images.
        Physica Med. 2018; 46: 32-44
        • Carvalho S.
        • Leijenaar R.T.H.
        • Troost E.G.C.
        • et al.
        18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) - A prospective externally validated study.
        PLoS ONE [Electronic Resource]. 2018; 13e0192859
        • Buizza G.
        • Toma-Dasu I.
        • Lazzeroni M.
        • et al.
        Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans.
        Physica Med. 2018; 54: 21-29
        • Astaraki M.
        • Wang C.
        • Buizza G.
        • Toma-Dasu I.
        • Lazzeroni M.
        • Smedby O.
        Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method.
        Physica Med. 2019; 60: 58-65https://doi.org/10.1016/j.ejmp.2019.03.024
        • van Timmeren J.E.
        • van Elmpt W.
        • Leijenaar R.T.H.
        • et al.
        Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients: Evaluation of the added prognostic value for overall survival and locoregional recurrence.
        Radiother Oncol. 2019; 136: 78-85https://doi.org/10.1016/j.radonc.2019.03.032
        • Lovinfosse P.
        • Janvary Z.L.
        • Coucke P.
        • et al.
        FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy.
        Eur J Nucl Med Mol Imaging. 2016; 43: 1453-1460
        • Krarup M.M.K.
        • Nygard L.
        • Vogelius I.R.
        • et al.
        Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool.
        Radiother Oncol. 2019; 144: 72-78https://doi.org/10.1016/j.radonc.2019.10.012
        • Pyka T.
        • Bundschuh R.A.
        • Andratschke N.
        • et al.
        Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy.
        Radiat Oncol. 2015; 10: 100
        • Bousabarah K.
        • Temming S.
        • Hoevels M.
        • et al.
        Radiomic analysis of planning computed tomograms for predicting radiation-induced lung injury and outcome in lung cancer patients treated with robotic stereotactic body radiation therapy.
        Strahlenther Onkol. 2019; 195: 830-842https://doi.org/10.1007/s00066-019-01452-7
        • Dissaux G.
        • Visvikis D.
        • Da-Ano R.
        • et al.
        Pre-treatment 18F-FDG PET/CT Radiomics predict local recurrence in patients treated with stereotactic radiotherapy for early-stage non-small cell lung cancer: a multicentric study.
        J Nucl Med. 2019; 15: 15https://doi.org/10.2967/jnumed.119.228106
        • Cook G.J.
        • Yip C.
        • Siddique M.
        • et al.
        Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy?.
        J Nucl Med. 2013; 54: 19-26
        • Arshad M.A.
        • Thornton A.
        • Lu H.
        • et al.
        Discovery of pre-therapy 2-deoxy-2-(18)F-fluoro-D-glucose positron emission tomography-based radiomics classifiers of survival outcome in non-small-cell lung cancer patients.
        Eur J Nucl Med Mol Imaging. 2018; 01: 01
        • Fried D.V.
        • Tucker S.L.
        • Zhou S.
        • et al.
        Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer.
        Int J Radiat Oncol Biol Phys. 2014; 90: 834-842
        • Fave X.
        • Zhang L.
        • Yang J.
        • et al.
        Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer.
        Sci Rep. 2017; 7: 588
        • Du Q.
        • Baine M.
        • Bavitz K.
        • et al.
        Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction.
        PLoS ONE. 2019; 14e0216480https://doi.org/10.1371/journal.pone.0216480
        • Mahon R.N.
        • Hugo G.D.
        • Weiss E.
        Repeatability of texture features derived from magnetic resonance and computed tomography imaging and use in predictive models for non-small cell lung cancer outcome.
        Phys Med Biol. 2019; 12: 12https://doi.org/10.1088/1361-6560/ab18d3
        • Shi L.
        • Rong Y.
        • Daly M.
        • et al.
        Cone-beam computed tomography-based delta-radiomics for early response assessment in radiotherapy for locally advanced lung cancer.
        Phys Med Biol. 2019; 15: 15https://doi.org/10.1088/1361-6560/ab3247
        • Starkov P.
        • Aguilera T.A.
        • Golden D.I.
        • et al.
        The use of texture-based radiomics CT analysis to predict outcomes in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy.
        Br J Radiol. 2019; 92: 20180228https://doi.org/10.1259/bjr.20180228
        • Oikonomou A.
        • Khalvati F.
        • Tyrrell P.N.
        • et al.
        Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy.
        Sci Rep. 2018; 8: 4003
        • Dong X.
        • Sun X.
        • Sun L.
        • et al.
        Early change in metabolic tumor heterogeneity during chemoradiotherapy and its prognostic value for patients with locally advanced non-small cell lung cancer.
        PLoS ONE [Electronic Resource]. 2016; 11e0157836
        • Liu W.
        • Sun X.
        • Qi Y.
        • et al.
        Integrated texture parameter of 18F-FDG PET may be a stratification factor for the survival of nonoperative patients with locally advanced non-small-cell lung cancer.
        Nucl Med Commun. 2018; 39: 732-740
        • Ahn S.Y.
        • Park C.M.
        • Park S.J.
        • et al.
        Prognostic value of computed tomography texture features in non-small cell lung cancers treated with definitive concomitant chemoradiotherapy.
        Invest Radiol. 2015; 50: 719-725
        • Satoh Y.
        • Motosugi U.
        • Nambu A.
        • Saito A.
        • Onishi H.
        Prognostic value of semiautomatic CT volumetry in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy.
        J Comput Assist Tomogr. 2016; 40: 343-350
        • Takeda K.
        • Takanami K.
        • Shirata Y.
        • et al.
        Clinical utility of texture analysis of 18F-FDG PET/CT in patients with stage I lung cancer treated with stereotactic body radiotherapy.
        J Radiat Res. 2017; 58: 862-869