PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology

M Sollini, L Cozzi, L Antunovic, A Chiti, M Kirienko, M Sollini, L Cozzi, L Antunovic, A Chiti, M Kirienko

Abstract

Imaging with positron emission tomography (PET)/computed tomography (CT) is crucial in the management of cancer because of its value in tumor staging, response assessment, restaging, prognosis and treatment responsiveness prediction. In the last years, interest has grown in texture analysis which provides an "in-vivo" lesion characterization, and predictive information in several malignances including NSCLC; however several drawbacks and limitations affect these studies, especially because of lack of standardization in features calculation, definitions and methodology reporting. The present paper provides a comprehensive review of literature describing the state-of-the-art of FDG-PET/CT texture analysis in NSCLC, suggesting a proposal for harmonization of methodology.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Schematic representation of the process of selection of literature data included in the review.
Figure 2
Figure 2
Methodological approaches in image texture analysis (the most frequently evaluated PET features in lung cancer patients are reported as examples).
Figure 3
Figure 3
Example of tumor contouring using in (a) a threshold method at 50% of SUVmax and (b) a method based on an absolute SUV cut-off of 2.5. The ROI identified by using the absolute SUV cut-off of 2.5 is greater than that identified by the threshold method, as shown by axial (top), sagittal (right), and coronal (left) images (same slices).

References

    1. Tixier F, et al. Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in 18F-FDG PET. J. Nucl. Med. 2012;53:693–700. doi: 10.2967/jnumed.111.099127.
    1. Sauter AW, Schwenzer N, Divine MR, Pichler BJ, Pfannenberg C. Image-derived biomarkers and multimodal imaging strategies for lung cancer management. Eur. J. Nucl. Med. Mol. Imaging. 2015;42:634–643. doi: 10.1007/s00259-014-2974-5.
    1. Meignan M, Itti E, Gallamini A, Younes A. FDG PET/CT imaging as a biomarker in lymphoma. Eur. J. Nucl. Med. Mol. Imaging. 2015;42:623–633. doi: 10.1007/s00259-014-2973-6.
    1. Differding S, Hanin F-X, Grégoire V. PET imaging biomarkers in head and neck cancer. Eur. J. Nucl. Med. Mol. Imaging. 2015;42:613–622. doi: 10.1007/s00259-014-2972-7.
    1. Picchio M, et al. Predictive value of pre-therapy (18)F-FDG PET/CT for the outcome of (18)F-FDG PET-guided radiotherapy in patients with head and neck cancer. Eur. J. Nucl. Med. Mol. Imaging. 2014;41:21–31. doi: 10.1007/s00259-013-2528-2.
    1. Guo W, et al. Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J. Med. imaging (Bellingham, Wash.) 2015;2:41007. doi: 10.1117/1.JMI.2.4.041007.
    1. Wang J, et al. Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study. PLoS One. 2015;10:e0143308. doi: 10.1371/journal.pone.0143308.
    1. Yip SSF, et al. Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients. Front. Oncol. 2016;6:72. doi: 10.3389/fonc.2016.00072.
    1. Hyun SH, et al. Intratumoral heterogeneity of (18)F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma. Eur. J. Nucl. Med. Mol. Imaging. 2016
    1. Rahim MK, et al. Recent Trends in PET Image Interpretations Using Volumetric and Texture-based Quantification Methods in Nuclear Oncology. Nucl. Med. Mol. Imaging (2010). 2014;48:1–15. doi: 10.1007/s13139-013-0260-2.
    1. O’Connor JPB, et al. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin. Cancer Res. 2015;21:249–257. doi: 10.1158/1078-0432.CCR-14-0990.
    1. Aerts HJ, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.
    1. Win T, et al. Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer. Clin. Cancer Res. 2013;19:3591–3599. doi: 10.1158/1078-0432.CCR-12-1307.
    1. Chicklore S, et al. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur. J. Nucl. Med. Mol. Imaging. 2013;40:133–140. doi: 10.1007/s00259-012-2247-0.
    1. Buvat I, Orlhac F, Soussan M. Tumor Texture Analysis in PET: Where Do We Stand? J. Nucl. Med. 2015;56:1642–1644. doi: 10.2967/jnumed.115.163469.
    1. Zorzela L, et al. PRISMA harms checklist : improving harms reporting in systematic reviews. BMJ. 2016;352:i157. doi: 10.1136/bmj.i157.
    1. Al-Kadi OS, Watson D. Texture analysis of aggressive and nonaggressive lung tumor CE CT images. IEEE Trans. Biomed. Eng. 2008;55:1822–1830. doi: 10.1109/TBME.2008.919735.
    1. Ganeshan B, Miles KA, Young RCD, Chatwin CR. Hepatic entropy and uniformity: additional parameters that can potentially increase the effectiveness of contrast enhancement during abdominal CT. Clin. Radiol. 2007;62:761–768. doi: 10.1016/j.crad.2007.03.004.
    1. Brown Ra, Frayne R. A comparison of texture quantification techniques based on the Fourier and S transforms. Med. Phys. 2008;35:4998–5008. doi: 10.1118/1.2992051.
    1. Craciunescu OI, Das SK, Clegg ST. Dynamic contrast-enhanced MRI and fractal characteristics of percolation clusters in two-dimensional tumor blood perfusion. J. Biomech. Eng. 1999;121:480–486. doi: 10.1115/1.2835076.
    1. Goh V, Sanghera B, Wellsted DM, Sundin J, Halligan S. Assessment of the spatial pattern of colorectal tumour perfusion estimated at perfusion CT using two-dimensional fractal analysis. Eur. Radiol. 2009;19:1358–1365. doi: 10.1007/s00330-009-1304-y.
    1. Dettori L, Semler L. A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography. Comput. Biol. Med. 2007;37:486–498. doi: 10.1016/j.compbiomed.2006.08.002.
    1. Sanghera, B. et al. Reproducibility of 2D and 3D Fractal Analysis Techniques for the Assessment of Spatial Heterogeneity of Regional Blood Flow in Rectal Cancer. (2012).
    1. Al-Kadi OS. Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images. Comput. Med. Imaging Graph. 2010;34:494–503. doi: 10.1016/j.compmedimag.2009.12.011.
    1. Tuceryan, M., Tuceryan, M., Jain, A. K. & Jain, A. K. The Handbook of Pattern Recognition and Computer Vision (2nd Edition), Texture Analysis. Pattern Recognit. 207–248, doi:10.1097/RCT.0b013e3181ec05e4 (1998).
    1. Yip SSF, Aerts HJWL. Applications and limitations of radiomics. Phys. Med. Biol. 2016;61:R150–R166. doi: 10.1088/0031-9155/61/13/R150.
    1. El Naqa I, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit. 2009;42:1162–1171. doi: 10.1016/j.patcog.2008.08.011.
    1. Drzymala RE, et al. Dose-volume histograms. Int. J. Radiat. Oncol. Biol. Phys. 1991;21:71–78. doi: 10.1016/0360-3016(91)90168-4.
    1. Vaidya M, et al. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother. Oncol. 2012;102:239–245. doi: 10.1016/j.radonc.2011.10.014.
    1. Nestle U, et al. Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer. J. Nucl. Med. 2005;46:1342–1348.
    1. Van Velden FHP, et al. Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur. J. Nucl. Med. Mol. Imaging. 2011;38:1636–1647. doi: 10.1007/s00259-011-1845-6.
    1. Boellaard R, et al. A novel cumulative SUV- volume histogram method for parameterizing heterogeneous tumour tracer uptake in oncology FDG PET studies. Eur. J. Nucl. Med. Mol. Imaging. 2010;37:S261.
    1. Haralick R, Shanmugam K, Dinstein I. Texture Features for Image Classification. IEEE Trans Sys Man Cyb SMC. 1973;3:610–621. doi: 10.1109/TSMC.1973.4309314.
    1. Yan R, et al. Detection of Myocardial Metabolic Abnormalities by 18F-FDG PET/CT and Corresponding Pathological Changes in Beagles with Local Heart Irradiation. Korean J. Radiol. 2015;16:919–928. doi: 10.3348/kjr.2015.16.4.919.
    1. Tixier F, et al. Visual Versus Quantitative Assessment of Intratumor 18F-FDG PET Uptake Heterogeneity: Prognostic Value in Non-Small Cell Lung Cancer. J. Nucl. Med. 2014;55:1235–1241. doi: 10.2967/jnumed.113.133389.
    1. Lovinfosse, 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, doi:10.1007/s00259-016-3314-8 (2016).
    1. Miwa K, et al. FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules. Eur. J. Radiol. 2014;83:715–719. doi: 10.1016/j.ejrad.2013.12.020.
    1. Grigsby PW, Siegel BA, Dehdashti F, Rader J, Zoberi I. Posttherapy [18F] fluorodeoxyglucose positron emission tomography in carcinoma of the cervix: response and outcome. J. Clin. Oncol. 2004;22:2167–2171. doi: 10.1200/JCO.2004.09.035.
    1. Greven KM. Positron-emission tomography for head and neck cancer. Semin. Radiat. Oncol. 2004;14:121–129. doi: 10.1053/j.semradonc.2003.12.005.
    1. Leijenaar RTH, et al. Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol. 2013;52:1391–1397. doi: 10.3109/0284186X.2013.812798.
    1. Velden, F. H. P. V et al. Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [18 F] FDG-PET/CT Studies : Impact of Reconstruction and Delineation. Mol Imaging Biol, doi:10.1007/s11307-016-0940-2 (2016).
    1. Oliver JA, et al. Variability of Image Features Computed from Conventional and Respiratory-Gated PET/CT Images of Lung Cancer. Transl. Oncol. 2015;8:524–534. doi: 10.1016/j.tranon.2015.11.013.
    1. Depeursinge A, Foncubierta-Rodriguez A, Van De Ville D, Müller H. Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities. Med. Image Anal. 2014;18:176–196. doi: 10.1016/j.media.2013.10.005.
    1. Hatt M, et al. Impact of Tumor Size and Tracer Uptake Heterogeneity in 18F-FDG PET and CT Non-Small Cell Lung Cancer Tumor Delineation. J. Nucl. Med. 2011;52:1690–1697. doi: 10.2967/jnumed.111.092767.
    1. Dong X, et al. Intra-tumour (18) F-FDG uptake heterogeneity decreases the reliability on target volume definition with positron emission tomography/computed tomography imaging. J. Med. Imaging Radiat. Oncol. 2015;d:338–345. doi: 10.1111/1754-9485.12289.
    1. Orlhac F, et al. Tumor Texture Analysis in 18F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis. J. Nucl. Med. 2014;55:414–422. doi: 10.2967/jnumed.113.129858.
    1. Yan J, et al. Impact of Image Reconstruction Settings on Texture Features in 18 F-FDG PET. J nucl med. 2015;56:1667–1674. doi: 10.2967/jnumed.115.156927.
    1. Cheng NM, Dean Fang YH, Tsan DL, Hsu CH, Yen TC. Respiration-averaged CT for attenuation correction of PET Images - Impact on PET Texture Features in Non-Small Cell Lung Cancer Patients. PLoS One. 2016;11:1–15.
    1. Hofheinz F, et al. An automatic method for accurate volume delineation of heterogeneous tumors in PET. Med. Phys. 2013;40:82503. doi: 10.1118/1.4812892.
    1. Cui, H., Wang, X. & Feng, D. Automated localization and segmentation of lung tumor from PET-CT thorax volumes based on image feature analysis. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, 5384–5387 doi:10.1109/EMBC.2012.6347211 (2012).
    1. Cui H, Wang X, Zhou J, Eberl S. Topology polymorphism graph for lung tumor segmentation in PET-CT images. Phys. Med. Biol. 2015;4893:4893. doi: 10.1088/0031-9155/60/12/4893.
    1. Leijenaar RTH, et al. The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. Sci. Rep. 2015;5:11075. doi: 10.1038/srep11075.
    1. Orlhac F, Soussan M, Chouahnia K, Martinod E, Buvat I. 18F-FDG PET-derived textural indices reflect tissue-specific uptake pattern in non-small cell lung cancer. PLoS One. 2015;10:1–16. doi: 10.1371/journal.pone.0145063.
    1. Boellaard R, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur. J. Nucl. Med. Mol. Imaging. 2014;42:328–354. doi: 10.1007/s00259-014-2961-x.
    1. Tixier, F. et al. Comparison of tumor uptake heterogeneity characterization between static and parametric 18F-FDG PET images in Non-Small Cell Lung Cancer. J. Nucl. Med. 31 (2016).
    1. Yip S, et al. Comparison of texture features derived from static and respiratory-gated PET images in non-small cell lung cancer. PLoS One. 2014;9:e115510. doi: 10.1371/journal.pone.0115510.
    1. Gao X, et al. The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from 18F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer. Eur. J. Radiol. 2015;84:312–317. doi: 10.1016/j.ejrad.2014.11.006.
    1. Buvat I, Orlhac F, Soussan M. Tumor Texture Analysis in PET: Where Do We Stand? J. Nucl. Med. 2015;56:1642–1644. doi: 10.2967/jnumed.115.163469.
    1. Budiawan H, et al. Heterogeneity Analysis of 18F-FDG Uptake in Differentiating Between Metastatic and Inflammatory Lymph Nodes in Adenocarcinoma of the Lung: Comparison with Other Parameters and its Application in a Clinical Setting. Nucl. Med. Mol. Imaging (2010). 2013;47:232–241. doi: 10.1007/s13139-013-0216-6.
    1. Ha S, et al. Autoclustering of Non-small Cell Lung Carcinoma Subtypes on 18F-FDG PET Using Texture Analysis: A Preliminary Result. Nucl. Med. Mol. Imaging (2010). 2014;48:278–286. doi: 10.1007/s13139-014-0283-3.
    1. Kim D-H, et al. Prognostic Significance of Intratumoral Metabolic Heterogeneity on 18F-FDG PET/CT in Pathological N0 Non–Small Cell Lung Cancer. Clin. Nucl. Med. 2015;40:708–714. doi: 10.1097/RLU.0000000000000867.
    1. van Gómez López O, et al. Heterogeneity in [18 F] fluorodeoxyglucose positron emission tomography/computed tomography of non – small cell lung carcinoma and its relationship to metabolic parameters and pathologic staging. Mol. Imaging. 2014;13:1–12.
    1. Warburg O. On the origin of cancer cells. Science. 1956;123:309–314. doi: 10.1126/science.123.3191.309.
    1. Warburg O. On respiratory impairment in cancer cells. Science. 1956;124:269–270.
    1. Mathupala SP, Ko YH, Pedersen PL. Hexokinase-2 bound to mitochondria: cancer’s stygian link to the & quot; Warburg Effect & quot; and a pivotal target for effective therapy. Semin. Cancer Biol. 2009;19:17–24. doi: 10.1016/j.semcancer.2008.11.006.
    1. Frezza C, Gottlieb E. Mitochondria in cancer: Not just innocent bystanders. Semin. Cancer Biol. 2009;19:4–11. doi: 10.1016/j.semcancer.2008.11.008.
    1. Robey RB, Hay N. Is Akt the & quot; Warburg kinase & quot; ?-Akt-energy metabolism interactions and oncogenesis. Semin. Cancer Biol. 2009;19:25–31. doi: 10.1016/j.semcancer.2008.11.010.
    1. Dang CV. Glutaminolysis: supplying carbon or nitrogen or both for cancer cells? Cell Cycle. 2010;9:3884–3886. doi: 10.4161/cc.9.19.13302.
    1. Dang CV. Rethinking the Warburg effect with Myc micromanaging glutamine metabolism. Cancer Res. 2010;70:859–862. doi: 10.1158/0008-5472.CAN-09-3556.
    1. Dang CV. Enigmatic MYC Conducts an Unfolding Systems Biology Symphony. Genes Cancer. 2010;1:526–531. doi: 10.1177/1947601910378742.
    1. Nair VS, et al. Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer. Cancer Res. 2012;72:3725–3734. doi: 10.1158/0008-5472.CAN-11-3943.
    1. Chalkidou A, O’Doherty MJ, Marsden PK. False discovery rates in PET and CT studies with texture features: A systematic review. PLoS One. 2015;10:1–18. doi: 10.1371/journal.pone.0124165.
    1. Apostolova I, et al. Quantitative assessment of the asphericity of pretherapeutic FDG uptake as an independent predictor of outcome in NSCLC. BMC Cancer. 2014;14:896. doi: 10.1186/1471-2407-14-896.
    1. Hatt M, et al. 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J. Nucl. Med. 2015;56:38–44. doi: 10.2967/jnumed.114.144055.
    1. Kang S-R, et al. Intratumoral Metabolic Heterogeneity for Prediction of Disease Progression After Concurrent Chemoradiotherapy in Patients with Inoperable Stage III Non-Small-Cell Lung Cancer. Nucl. Med. Mol. Imaging (2010). 2014;48:16–25. doi: 10.1007/s13139-013-0231-7.
    1. Ohri, N. et al. Pretreatment 18FDG-PET Textural Features in Locally Advanced Non-Small Cell Lung Cancer: Secondary Analysis of ACRIN 6668/RTOG 0235. J. Nucl. Med. 1–30, doi:10.2967/jnumed.115.166934 (2016).
    1. Cook GJR, 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. doi: 10.2967/jnumed.112.107375.
    1. Cook GJR, et al. Non-Small Cell Lung Cancer Treated with Erlotinib: Heterogeneity of (18)F-FDG Uptake at PET-Association with Treatment Response and Prognosis. Radiology. 2015;276:883–893. doi: 10.1148/radiol.2015141309.
    1. Pyka T, 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. doi: 10.1186/s13014-015-0407-7.
    1. Carvalho S, et al. Prognostic value of metabolic metrics extracted from baseline PET images in NSCLC in non small cell lung cancer. Acta Oncol. 2013;52:1398–1404. doi: 10.3109/0284186X.2013.812795.
    1. Fried DV, 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. doi: 10.1148/radiol.2015142920.
    1. Fried DV, et al. Potential Use of 18F-fluorodeoxyglucose Positron Emission Tomography-Based Quantitative Imaging Features for Guiding Dose Escalation in Stage III Non-Small Cell Lung Cancer. Int. J. Radiat. Oncol. Biol. Phys. 2016;94:368–376. doi: 10.1016/j.ijrobp.2015.10.029.
    1. Desseroit, M.-C. et al. Development of a nomogram combining clinical staging with (18) F-FDG PET/CT image features in non-small-cell lung cancer stage I-III. Eur. J. Nucl. Med. Mol. Imaging, doi:10.1007/s00259-016-3325-5 (2016).
    1. Wu, J. & Rubin, D. L. Early-Stage Non – Small Cell Lung Cancer : Quantitative Imaging Characteristics of 18 F Fluorodeoxyglucose PET/CT Allow. Radiology (2016).
    1. Hatt, M. et al. Characterization of PET/CT images using texture analysis: the past, the present … any future? Eur. J. Nucl. Med. Mol. Imaging, doi:10.1007/s00259-016-3427-0 (2016).

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