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Molecular Imaging and Biology

, Volume 19, Issue 3, pp 456–468 | Cite as

A Novel Framework for Automated Segmentation and Labeling of Homogeneous Versus Heterogeneous Lung Tumors in [18F]FDG-PET Imaging

  • Motahare Soufi
  • Alireza Kamali-AslEmail author
  • Parham Geramifar
  • Arman RahmimEmail author
Research Article

Abstract

Purpose

Determination of intra-tumor high-uptake area using 2-deoxy-2-[18F]fluoro-d-glucose ([18F]FDG) positron emission tomography (PET) imaging is an important consideration for dose painting in radiation treatment applications. The aim of our study was to develop a framework towards automated segmentation and labeling of homogeneous vs. heterogeneous tumors in clinical lung [18F]FDG-PET with the capability of intra-tumor high-uptake region delineation.

Procedures

We utilized and extended a fuzzy random walk PET tumor segmentation algorithm to delineate intra-tumor high-uptake areas. Tumor textural feature (TF) analysis was used to find a relationship between tumor type and TF values. Segmentation accuracy was evaluated quantitatively utilizing 70 clinical [18F]FDG-PET lung images of patients with a total of 150 solid tumors. For volumetric analysis, the Dice similarity coefficient (DSC) and Hausdorff distance (HD) measures were extracted with respect to gold-standard manual segmentation. A multi-linear regression model was also proposed for automated tumor labeling based on TFs, including cross-validation analysis.

Results

Two-tailed t test analysis of TFs between homogeneous and heterogeneous tumors revealed significant statistical difference for size-zone variability (SZV), intensity variability (IV), zone percentage (ZP), proposed parameters II and III, entropy and tumor volume (p < 0.001), dissimilarity, high intensity emphasis (HIE), and SUVmin (p < 0.01). Lower statistical differences were observed for proposed parameter I (p = 0.02), and no significant differences were observed for SUVmax and SUVmean. Furthermore, the Spearman rank analysis between visual tumor labeling and TF analysis depicted a significant correlation for SZV, IV, entropy, parameters II and III, and tumor volume (0.68 ≤ ρ ≤ 0.84) and moderate correlation for ZP, HIE, homogeneity, dissimilarity, parameter I, and SUVmin (0.22 ≤ ρ ≤ 0.52), while no correlations were observed for SUVmax and SUVmean (ρ < 0.08). The multi-linear regression model for automated tumor labeling process resulted in R 2 and RMSE values of 0.93 and 0.14, respectively (p < 0.001), and generated tumor labeling sensitivity and specificity of 0.93 and 0.89. With respect to baseline random walk segmentation, the results showed significant (p < 0.001) mean DSC, HD, and SUVmean error improvements of 21.4 ± 11.5 %, 1.4 ± 0.8 mm, and 16.8 ± 8.1 % in homogeneous tumors and 7.4 ± 4.4 %, 1.5 ± 0.6 mm, and 7.9 ± 2.7 % in heterogeneous lesions. In addition, significant (p < 0.001) mean DSC, HD, and SUVmean error improvements were observed for tumor sub-volume delineations, namely 5 ± 2 %, 1.5 ± 0.6 mm, and 7 ± 3 % for the proposed Fuzzy RW method compared to RW segmentation.

Conclusion

We proposed and demonstrated an automatic framework for significantly improved segmentation and labeling of homogeneous vs. heterogeneous tumors in lung [18F]FDG-PET images.

Key words

Heterogeneous tumor delineation Automated PET image segmentation Random walk Fuzzy logic 

Notes

Compliance with Ethical Standards

Conflict of Interest

There authors declare they have no conflict of interest.

References

  1. 1.
    Juweid M, Cheson B (2006) Positron-emission tomography and assessment of cancer therapy. New Engl J Med 354:496–507CrossRefPubMedGoogle Scholar
  2. 2.
    Rahmim A, Wahl R (2006) An overview of clinical PET/CT. Iranian. J Nucl Med 14:1–14Google Scholar
  3. 3.
    de Geus-Oei LF, Vriens D, van Laarhoven HWM, van der Graaf WTA, Oyen WJG (2009) Monitoring and predicting response to therapy with 18F-FDG PET in colorectal cancer: a systematic review. J Nucl Med 50:43–54CrossRefGoogle Scholar
  4. 4.
    Orlhac F, Soussan M, Maisonobe JA et al (2014) 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 55:414–422CrossRefPubMedGoogle Scholar
  5. 5.
    Gerlinger M, Rowan AJ, Horswell S et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. New Engl J Med 366:883–892CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Bradshaw TJ, Bowen SR, Jallow N et al (2013) Heterogeneity in intratumor correlations of 18F-FDG, 18F-FLT, and 61Cu-ATSM PET in canine sinonasal tumors. J Nucl Med 54(11):1931–1937CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Basu S, Kwee T, Gatenby R et al (2011) Evolving role of molecular imaging with PET in detecting and characterizing heterogeneity of cancer tissue at the primary and metastatic sites, a plausible explanation for failed attempts to cure malignant disorders. Eur J Nucl Med Mol Imaging 38:987–991CrossRefPubMedGoogle Scholar
  8. 8.
    Tixier F, Le Rest CC, Hatt M et al (2011) Intratumor heterogeneity characterized by textural features on baseline (18)F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 52:369–378CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Hatt M, Le Rest CC, van Baardwijk A et al (2011) Impact of tumor size and tracer uptake heterogeneity in 18F-FDG PET and CT non small cell lung cancer tumor delineation. J Nucl Med 52:1690–1697CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Ford EC, Herman J, Yorke EL, Wahl RL (2009) 18F-FDG PET/CT for image guided and intensity-modulated radiotherapy. J Nucl Med 50:1655–1665CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Ling C, Humm J, Larson S et al (2000) Towards multidimensional radiotherapy (MD-CRT): biological imaging and biological conformality. Int J Radiat Oncol Biol Phys 47:10CrossRefGoogle Scholar
  12. 12.
    Riegel A, Berson A, Destian S et al (2006) Variability of gross tumor volume delineation in head-andneck cancer using CT and PET/CT fusion. Int. J. Radiat. Oncol., Biol. Phys 65(3):726–732CrossRefGoogle Scholar
  13. 13.
    Bradley J, Thorstad WL, Mutic S et al (2004) Impact of FDG-PET on radiation therapy volume delineation in non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 59(1):78–86CrossRefPubMedGoogle Scholar
  14. 14.
    Nestle U, Kremp S, Schaefer-Schuler A et al (2005) 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 46:1342–1348PubMedGoogle Scholar
  15. 15.
    Hatt M, Le Rest CC, Descourt P et al (2010) Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiation Oncology Biol Phys 77:301–308CrossRefGoogle Scholar
  16. 16.
    Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28:1–17CrossRefGoogle Scholar
  17. 17.
    Soufi M, Kamali-Asl A, Geramifar P et al (2016) Combined fuzzy logic and random walker algorithm for PET image tumor delineation. Nucl Med Comm 37:171–181CrossRefGoogle Scholar
  18. 18.
    Onoma DP, Ruan S, Gardin I, et al. (2012) 3D random walk based segmentation for lung tumor delineation in PET imaging. Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on; p. 1260–3Google Scholar
  19. 19.
    Soufi M, Kamali Asl AR, Geramifar P (2015) Random walk algorithm seed localization parameters in lung positron emission tomography (PET) images. Med Phys 42Google Scholar
  20. 20.
    Fechter T, Mix M, Gardin I et al (2013) Malignant glioma delineation in amino acid PET-images using a 3D random walk approach. Intl J Radiat Oncol Biol Physics 87:S622CrossRefGoogle Scholar
  21. 21.
    Hui C, Xiuying W, Fulham M, Feng DD (2013) Prior knowledge enhanced random walk for lung tumor segmentation from low-contrast CT images. Eng Med Biol Soc (EMBC), 2013 35th Annual International Conference of the IEEE:6071–6074Google Scholar
  22. 22.
    Bagci U, Udupa JK, Mendhiratta N et al (2013) Joint segmentation of anatomical and functional images: applications in quantification of lesions from PET, PET-CT, MRI-PET, and MRI-PET-CT images. Med Image Anal 17:929–945CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Bagci U, Udupa J, Yao J, Mollura D. (2012) Co-segmentation of functional and anatomical images. In: Proc. Med Image Computing and Computer-Assisted Intervention:459–67Google Scholar
  24. 24.
    Bagci U, Yao J, Caban J, et al. (2011) A graph-theoretic approach for segmentation of pet images. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. IEEE:8479–82Google Scholar
  25. 25.
    Kaur EK, Mutenja EV (2010) Fuzzy logic based image edge detection algorithm in MATLAB. Intl J Computer Appl 1:55–58Google Scholar
  26. 26.
    Kumar D J, Mohan V. (2014) Edge detection in the medical MR brain image based on fuzzy logic technique. Information Communication and Embedded Systems (ICICES), 2014 International Conference on; p. 1–9Google Scholar
  27. 27.
    Rashmi KA, Kusagur DA (2012) An improved fast edge detection for medical image based on fuzzy techniques. Fuzzy Systems 4:147–150Google Scholar
  28. 28.
    Eary JF, O'Sullivan F, O'Sullivan J, Conrad EU (2008) Spatial heterogeneity in sarcoma (18)F-FDG uptake as a predictor of patient outcome. J Nucl Med 49:1973–1979CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    El Naqa I, Grigsby PW, Apte A et al (2009) Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recogn 42:1162–1171CrossRefGoogle Scholar
  30. 30.
    van Velden FHP, Cheebsumon P, Yaqub M et al (2011) 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 I 38:1636–1647CrossRefGoogle Scholar
  31. 31.
    Asselin MC, O’Connor JPB, Boellaard R et al (2012) Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer 48:447–455CrossRefPubMedGoogle Scholar
  32. 32.
    Vriens D, Disselhorst JA, Oyen WJG et al (2012) Quantitative assessment of heterogeneity in tumor metabolism using FDG-PET. Int. J Radiat Oncol 82:E725–EE31CrossRefGoogle Scholar
  33. 33.
    Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Kumar V, YH G, Basu S, Berglund A et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Chicklore S, Goh V, Siddique M et al (2013) Quantifying tumour heterogeneity in F-18-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol I 40:133–140CrossRefGoogle Scholar
  36. 36.
    Tixier F, Hatt M, Valla C et al (2014) Visual versus quantitative assessment of intratumor F-18-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer. J Nucl Med 55:1235–1241CrossRefPubMedGoogle Scholar
  37. 37.
    Aerts HJWL, Velazquez ER, Leijenaar RTH et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. doi: 10.1038/ncomms5006 PubMedPubMedCentralGoogle Scholar
  38. 38.
    Hatt M, Majdoub M, Vallieres M et al (2015) F-18-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 56:38–44CrossRefPubMedGoogle Scholar
  39. 39.
    Rahmim A, Schmidtlein CR, Jackson A et al (2016) A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction. Phys Med Biol 61:227–242CrossRefPubMedGoogle Scholar
  40. 40.
    Tixier F, Groves AM, Goh V et al (2014) Correlation of intra-tumor 18F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer. PLoS One 9:1–7CrossRefGoogle Scholar
  41. 41.
    Tixier F, Hatt M, Le Rest CC et al (2012) Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in F-18-FDG PET. J Nucl Med 53:693–700CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Leijenaar RTH, Carvalho S, Velazquez ER et al (2013) Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol 52:1391–1397CrossRefPubMedGoogle Scholar
  43. 43.
    Kim K, Kim SJ, Kim IJ et al (2012) Prognostic value of volumetric parameters measured by F-18 FDG PET/CT in surgically resected nonsmall- cell lung cancer. Nucl Med Commun 33:613–620CrossRefPubMedGoogle Scholar
  44. 44.
    Liao S, Penney BC, Wroblewski K et al (2012) Prognostic value of metabolic tumor burden on 18F-FDG PET in nonsurgical patients with non-small cell lung cancer. Eur J Nucl Med Mol Imaging 39:27–38CrossRefPubMedGoogle Scholar
  45. 45.
    Hyun SH, Ahn H, Kim H et al (2014) Volume-based assessment by 18F-FDG PET/CT predicts survival in patients with stage III non-small-cell lung cancer. Eur J Nucl Med Mol Imaging 41:50–58CrossRefPubMedGoogle Scholar
  46. 46.
    Galavis P, Hollensen C, Jallow N,P et al (2010) Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol 49:1012–1016CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Hatt M, Tixier F, Le Rest CC et al (2013) Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. European Journal of Nuclear Medicine Molecular Imaging 40:1662–1671CrossRefPubMedGoogle Scholar
  48. 48.
    Lu L, Lv W, Jiang J et al (2016) Robustness of radiomic features in 11C-choline and 18F-FDG PET/CT imaging of nasopharyngeal carcinoma: impact of segmentation and discretization (supplement). Molec Imag Biol. doi: 10.1007/s11307-016-0973-6 Google Scholar
  49. 49.
    Grkovski M, Apte A, Schwartz J, et al. (2015) Reproducibility of F-18-FMISO intratumor distribution and texture features in NSCLC. J Nucl Med 56Google Scholar
  50. 50.
    van Velden F, Kramer G, Frings V, et al. (2016) Repeatability of radiomic features in non-small-cell lung cancer [18F]FDG-PET/CT studies: impact of reconstruction and delineation. Molec. Imag. Biol. In PressGoogle Scholar
  51. 51.
    Ashrafinia S, Gonzalez EM, Mohy-ud-Din H et al (2016) Adaptive PSF modeling for enhanced heterogeneity quantification in oncologic PET imaging. Nuc Med Med 57(suppl. 2):479Google Scholar
  52. 52.
    Willaime JM, Turkheimer FE, Kenny LM, Aboagye EO (2013) Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography. Phys Med Biol 58:187–203CrossRefPubMedGoogle Scholar
  53. 53.
    Dice L (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302CrossRefGoogle Scholar
  54. 54.
    Cignoni P, Rocchini C, Scopigno R (1998) Metro: measuring error on simplified surfaces. Computer Graphics Forum 18:167–174CrossRefGoogle Scholar
  55. 55.
    Song Q, Bai J, Han D et al (2013) Optimal Co-segmentation of tumor in PET-CT images with context information. IEEE Trans Med Imaging 32:1685–1697CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Belhassen S, Zaidi H (2010) A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET. Med Phys 37:1309–1324CrossRefPubMedGoogle Scholar
  57. 57.
    Abdoli M, Dierckx RAJO, Zaidi H (2012) Deformable model-based PET segmentation for heterogeneous tumor volume delineation. IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) M22-45:3947–3951CrossRefGoogle Scholar

Copyright information

© World Molecular Imaging Society 2016

Authors and Affiliations

  1. 1.Department of Radiation Medicine EngineeringShahid Beheshti UniversityTehranIran
  2. 2.Research Center for Nuclear Medicine, Shariati HospitalTehran University of Medical SciencesTehranIran
  3. 3.Department of RadiologyJohns Hopkins UniversityBaltimoreUSA
  4. 4.Department of Electrical & Computer EngineeringJohns Hopkins UniversityBaltimoreUSA

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