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Advances in Imaging and Automated Quantification of Malignant Pulmonary Diseases: A State-of-the-Art Review

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Abstract

Quantitative imaging in lung cancer is a rapidly evolving modality in radiology that is changing clinical practice from a qualitative analysis of imaging features to a more dynamic, spatial, and phenotypical characterization of suspected lesions. Some quantitative parameters, such as the use of 18F-FDG PET/CT-derived standard uptake values (SUV), have already been incorporated into current practice as it provides important information for diagnosis, staging, and treatment response of patients with lung cancer. A growing body of evidence is emerging to support the use of quantitative parameters from other modalities. CT-derived volumetric assessment, CT and MRI lung perfusion scans, and diffusion-weighted MRI are some of the examples. Software-assisted technologies are the future of quantitative analyses in order to decrease intra- and inter-observer variability. In the era of “big data”, widespread incorporation of radiomics (extracting quantitative information from medical images by converting them into minable high-dimensional data) will allow medical imaging to surpass its current status quo and provide more accurate histological correlations and prognostic value in lung cancer. This is a comprehensive review of some of the quantitative image methods and computer-aided systems to the diagnosis and follow-up of patients with lung cancer.

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Abbreviations

18F-FDG PET/CT:

Fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography

ADC:

Apparent diffusion coefficient

CAD:

Computer-aided diagnosis

CT:

Computed tomography

DCE:

Dynamic contrast-enhanced

DTP:

Dual time point imaging technique

DWI:

Diffusion-weighted imaging

LSR:

Lesion-to-spinal cord ratio

MRI:

Magnetic resonance imaging

MRI-SI:

Magnetic resonance imaging signal intensity

MVD:

Microvessel density

NSCLC:

Non-small cell lung cancer

RECIST:

Response evaluation criteria in solid tumors

ROI:

Regions of interest

SI:

Signal intensity

SUV:

Standardized uptake value

VDT:

Volume doubling time

References

  1. McMahon PM, Kong CY, Johnson BE et al (2008) Estimating long-term effectiveness of lung cancer screening in the Mayo CT screening study. Radiology 248:278–287

    Article  Google Scholar 

  2. Harders SW, Balyasnikowa S, Fischer BM (2014) Functional imaging in lung cancer. Clin Physiol Funct Imaging 34:340–355

    Article  CAS  Google Scholar 

  3. Dela Cruz CS, Tanoue LT, Matthay RA (2011) Lung cancer: epidemiology, etiology, and prevention. Clin Chest Med 32:605–644

    Article  Google Scholar 

  4. Yankeelov TE, Mankoff DA, Schwartz LH et al (2016) Quantitative imaging in cancer clinical trials. Clin Cancer Res: Off J Am Assoc Cancer Res 22(2):284–290

    Article  Google Scholar 

  5. UyBico SJ, Wu CC, Suh RD et al (2010) Lung cancer staging essentials: the new TNM staging system and potential imaging pitfalls. Radiographics 30:1163–1181

    Article  Google Scholar 

  6. Halpern BS, Schiepers C, Weber WA et al (2005) Presurgical staging of non-small cell lung cancer: positron emission tomography, integrated positron emission tomography/CT, and software image fusion. Chest 128:2289–2297

    Article  Google Scholar 

  7. Henzler T, Schmid-Bindert G, Schoenberg SO et al (2010) Diffusion and perfusion MRI of the lung and mediastinum. Eur J Radiol 76:329–336

    Article  Google Scholar 

  8. Matoba M, Tonami H, Kondou T et al (2007) Lung carcinoma: diffusion-weighted MR imaging—preliminary evaluation with apparent diffusion coefficient. Radiology 243:570–577

    Article  Google Scholar 

  9. García-Figueiras R, Goh VJ, Padhani AR et al (2013) CT perfusion in oncologic imaging: a useful tool? Am J Roentgenol 200:8–19

    Article  Google Scholar 

  10. Horeweg N, van Rosmalen J, Heuvelmans MA et al (2014) Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. Lancet Oncol 15:1332–1341

    Article  Google Scholar 

  11. MacMahon H, Naidich DP, Goo JM et al (2017) Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology 284(1):228–243

    Article  Google Scholar 

  12. Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247

    Article  CAS  Google Scholar 

  13. Galizia M, Töre H, Chalian H et al (2011) Evaluation of hepatocellular carcinoma size using two-dimensional and volumetric analysis. Acad Radiol 14:1555–1560

    Article  Google Scholar 

  14. Marten K, Auer F, Schmidt S et al (2007) Automated CT volumetry of pulmonary metastases: the effect of a reduced growth threshold and target lesion number on the reliability of therapy response assessment using RECIST criteria. Eur Radiol 17:2561–2571

    Article  Google Scholar 

  15. Vogel M, Schmücker S, Maksimovic O et al (2012) Reduction in growth threshold for pulmonary metastases: an opportunity for volumetry and its impact on treatment decisions. Br J Radiol 85:959–964

    Article  CAS  Google Scholar 

  16. Dicken V, Bornemann L, Moltz JH et al (2015) Comparison of volumetric and linear serial CT assessments of lung metastases in renal cell carcinoma patients in a clinical phase IIB study. Acad Radiol 22:619–625

    Article  Google Scholar 

  17. Bankier AA, MacMahon H, Goo JM et al (2017) Recommendations for measuring pulmonary nodules at CT: a statement from the Fleischner Society. Radiology 285:584–600

    Article  Google Scholar 

  18. Yousaf-Khan U, van der Aalst C, de Jong PA et al (2017) Final screening round of the NELSON lung cancer screening trial: the effect of a 2.5-year screening interval. Thorax 72(1):48–56

    Article  Google Scholar 

  19. de Hoop B, Gietema H, van Ginneken B et al (2009) A comparison of six software packages for evaluation of solid lung nodules using semiautomated volumetry: what is the minimum increase in size to detect growth in repeated CT examinations. Eur Radiol 19:800–808

    Article  Google Scholar 

  20. Zhao YR, van Ooijen PM, Dorrius MD et al (2014) Comparison of three software systems for semi-automatic volumetry of pulmonary nodules on baseline and follow-up CT examinations. Acta Radiol 55:691–698

    Article  Google Scholar 

  21. Kuhnert G, Boellaard R, Sterzer S et al (2016) Impact of PET/CT image reconstruction methods and liver uptake normalization strategies on quantitative image analysis. Eur J Nucl Med Mol Imaging 43:249–258

    Article  CAS  Google Scholar 

  22. Boellaard R, Delgado-Bolten R, Oyen WJG et al (2015) FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 2.0. Eur J Nucl Med Mol Imaging 42:328–354

    Article  CAS  Google Scholar 

  23. Markovina S, Duan F, Snyder BS et al (2015) Regional lymph node uptake of [(18)F]fluorodeoxyglucose after definitive chemoradiation therapy predicts local-regional failure of locally advanced non-small cell lung cancer: results of ACRIN 6668/RTOG 0235. Int J Radiat Oncol Biol Phys 93:597–605

    Article  Google Scholar 

  24. Paesmans M, Garcia C, Wong CO et al (2015) Primary tumour standardised uptake value is prognostic in nonsmall cell lung cancer: a multivariate pooled analysis of individual data. Eur Respir J 46:1751–1761

    Article  CAS  Google Scholar 

  25. Cerfolio RJ, Bryant AS, Ohja B et al (2005) The maximum standardized uptake values on positron emission tomography of a non-small cell lung cancer predict stage, recurrence, and survival. J Thorac Cardiovasc Surg 130:151–159

    Article  Google Scholar 

  26. Nahmias C, Hanna WT, Wahl LM et al (2007) Time course of early response to chemotherapy in non-small cell lung cancer patients with 18F-FDG PET/CT. J Nucl Med 48:744–751

    Article  CAS  Google Scholar 

  27. Gupta NC, Tamim WJ, Graeber GG et al (2001) Mediastinal lymph node sampling following positron emission tomography with fluorodeoxyglucose imaging in lung cancer staging. Chest 120:521–527

    Article  CAS  Google Scholar 

  28. Roberts PF, Follette DM, von Haag D et al (2000) Factors associated with false-positive staging of lung cancer by positron emission tomography. Ann Thorac Surg 70:1154–1159

    Article  CAS  Google Scholar 

  29. Nakayama M, Okizaki A, Ishitoya S et al (2013) Dual-time-point F-18 FDG PET/CT imaging for differentiating the lymph nodes between malignant lymphoma and benign lesions. Ann Nucl Med 27:163–169

    Article  CAS  Google Scholar 

  30. Kumar R, Loving VA, Chauhan A et al (2005) Potential of dual-time-point imaging to improve breast cancer diagnosis with (18)F-FDG PET. J Nucl Med 46:1819–1824

    PubMed  Google Scholar 

  31. Sathekge MM, Maes A, Pottel H et al (2010) Dual time-point FDG PET-CT for differentiating benign from malignant solitary pulmonary nodules in a TB endemic area. S Afr Med J 100:598–601

    Article  Google Scholar 

  32. Kaneko K, Sadashima E, Irie K et al (2013) Assessment of FDG retention differences between the FDG-avid benign pulmonary lesion and primary lung cancer using dual-time-point FDG-PET imaging. Ann Nucl Med 27:392–399

    Article  Google Scholar 

  33. Saleh Farghaly HR, Mohamed Sayed MH, Nasr HA et al (2015) Dual time point fluorodeoxyglucose positron emission tomography/computed tomography in differentiation between malignant and benign lesions in cancer patients. Does it always work? Indian J Nucl Med 30:314–319

    Article  Google Scholar 

  34. Wong CS, Gong N, Chu YC et al (2012) Correlation of measurements from diffusion weighted MR imaging and FDG PET/CT in GIST patients: ADC versus SUV. Eur J Radiol 81:2122–2126

    Article  Google Scholar 

  35. Usaro A, Ruokonen E, Takala J (1995) Estimation of splanchnic blood flow by the Fick principle in man and problems in the use of indocyanine green. Cardiovasc Res 30:106–112

    Article  Google Scholar 

  36. Bevilacqua A, Barone D, Malavasi S et al (2014) Quantitative assessment of effects of motion compensation for liver and lung tumors in CT perfusion. Acad Radiol 21:1416–1426

    Article  Google Scholar 

  37. Li Y, Yang ZG, Chen TW et al (2008) Peripheral lung carcinoma: correlation of angiogenesis and first-pass perfusion parameters of 64-detector row CT. Lung Cancer 61:44–53

    Article  Google Scholar 

  38. Ma SH, Le HB, Jia BH et al (2008) Peripheral pulmonary nodules: relationship between multi-slice spiral CT perfusion imaging and tumor angiogenesis and VEGF expression. BMC Cancer 8:186

    Article  Google Scholar 

  39. Ma S-H, Le H-B, Jia B et al (2008) Peripheral pulmonary nodules: relationship between multi-slice spiral CT perfusion imaging and tumor angiogenesis and VEGF expression. BMC Cancer 8:186

    Article  Google Scholar 

  40. Wang J, Wu N, Cham MD et al (2009) Tumor response in patients with advanced non-small cell lung cancer: perfusion CT evaluation of chemotherapy and radiation therapy. AJR Am J Roentgenol 193:1090–1096

    Article  Google Scholar 

  41. Huellner MW, Collen TD, Gut P et al (2014) Multiparametric PET/CT-perfusion does not add significant additional information for initial staging in lung cancer compared with standard PET/CT. EJNMMI Res 4:6

    Article  Google Scholar 

  42. Mirsadraee S, van Beek EJR (2015) Functional imaging: computed tomography and MRI. Clin Chest Med 36:349–363

    Article  Google Scholar 

  43. O’Connor JP, Tofts PS, Miles KA et al (2011) Dynamic contrast-enhanced imaging techniques: CT and MRI. Br J Radiol 84:S112–S120

    Article  Google Scholar 

  44. Petralia G, Preda L, D’Andrea G et al (2010) CT perfusion in solid-body tumours. Part I: technical issues. Radiol Med 115:843–857

    Article  CAS  Google Scholar 

  45. Li Y, Yang Z-G, Chen T-W, Yu J-Q, Sun J-Y, Chen H-J (2010) First-pass perfusion imaging of solitary pulmonary nodules with 64-detector row CT: comparison of perfusion parameters of malignant and benign lesions. Br J Radiol 83(993):785–790

    Article  CAS  Google Scholar 

  46. Yuan X, Zhang J, Quan C et al (2013) Differentiation of malignant and benign pulmonary nodules with first-pass dual-input perfusion CT. Eur Radiol 23(9):2469–2474

    Article  Google Scholar 

  47. Ohno Y, Koyama H, Matsumoto K et al (2011) Differentiation of malignant and benign pulmonary nodules with quantitative first-pass 320-detector row perfusion CT versus FDG PET/CT. Radiology 258(2):599–609

    Article  Google Scholar 

  48. Jiang B, Liu H, Zhou D (2016) Diagnostic and clinical utility of dynamic contrast-enhanced MR imaging in indeterminate pulmonary nodules: a metaanalysis. Clin Imaging 40:1219–1225

    Article  Google Scholar 

  49. Cheng JC, Yuan A, Chen JH et al (2013) Early detection of Lewis lung carcinoma tumor control by irradiation using diffusion-weighted and dynamic contrast-enhanced MRI. PLoS ONE 8:e62762

    Article  CAS  Google Scholar 

  50. Koenigkam-Santos M, Optazaite E, Sommer G et al (2015) Contrast-enhanced magnetic resonance imaging of pulmonary lesions: description of a technique aiming clinical practice. Eur J Radiol 84:185–192

    Article  Google Scholar 

  51. Schaefer JF, Vollmar J, Schick F et al (2004) Solitary pulmonary nodules: dynamic contrast-enhanced MR imaging–perfusion differences in malignant and benign lesions. Radiology 232:544–553

    Article  Google Scholar 

  52. Bell LC, Wang K, Munoz Del Rio A et al (2015) Comparison of models and contrast agents for improved signal and signal linearity in dynamic contrast-enhanced pulmonary magnetic resonance imaging. Invest Radiol 50:174–178

    Article  CAS  Google Scholar 

  53. Ohba Y, Nomori H, Mori T et al (2009) Is diffusion-weighted magnetic resonance imaging superior to positron emission tomography with fludeoxyglucose F 18 in imaging non-small cell lung cancer? J Thorac Cardiovasc Surg 138:439–445

    Article  Google Scholar 

  54. Li B, Li Q, Chen C et al (2014) A systematic review and meta-analysis of the accuracy of diffusion-weighted MRI in the detection of malignant pulmonary nodules and masses. Acad Radiol 21:21–29

    Article  Google Scholar 

  55. Wu LM, Xu JR, Hua J et al (2013) Can diffusion-weighted imaging be used as a reliable sequence in the detection of malignant pulmonary nodules and masses? Magn Reson Imaging 31:235–246

    Article  Google Scholar 

  56. Usuda K, Zhao XT, Sagawa M et al (2011) Diffusion-weighted imaging is superior to positron emission tomography in the detection and nodal assessment of lung cancers. Ann Thorac Surg 91:1689–1695

    Article  Google Scholar 

  57. Regier M, Derlin T, Schwarz D et al (2012) Diffusion weighted MRI and 18F-FDG PET/CT in non-small cell lung cancer (NSCLC): does the apparent diffusion coefficient (ADC) correlate with tracer uptake (SUV)? Eur J Radiol 81:2913–2918

    Article  CAS  Google Scholar 

  58. Pauls S, Schmidt SA, Juchems MS et al (2012) Diffusion-weighted MR imaging in comparison to integrated [18F]-FDG PET/CT for N-staging in patients with lung cancer. Eur J Radiol 81:178–182

    Article  Google Scholar 

  59. Henz-Concatto N, Watte G, Marchiori E et al (2016) Magnetic resonance imaging of pulmonary nodules: accuracy in a granulomatous disease-endemic region. Eur Radiol 26:2915–2920

    Article  Google Scholar 

  60. Hochhegger B, Marchiori E, dos Reis DQ et al (2012) Chemical-shift MRI of pulmonary hamartomas: initial experience using a modified technique to assess nodule fat. AJR Am J Roentgenol 199:W331–W334

    Article  Google Scholar 

  61. Gillies R, Kinahan P, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  62. Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31:198–211

    Article  Google Scholar 

  63. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:400665

    Google Scholar 

  64. Ganeshan B, Panayiotou E, Burnand K et al (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22:796–802

    Article  Google Scholar 

  65. Fried DV, Tucker SL, Zhou S et al (2014) Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. Int J Radiat Oncol Biol Phys 90:834–842

    Article  Google Scholar 

  66. Yoon HJ, Sohn I, Cho JH et al (2015) Decoding tumor phenotypes for ALK, ROS1, and RET fusions in lung adenocarcinoma using a radiomics approach. Medicine (Baltimore) 94:e1753

    Article  CAS  Google Scholar 

  67. Ferreira-Junior JR, Koenigkam-Santos M, Cipriano FEG et al (2018) Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput Methods Programs Biomed 159:23–30

    Article  Google Scholar 

  68. Yang J, Zhang L, Fave X (2016) Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors. Comput Med Imaging Graph 48:1–8

    Article  CAS  Google Scholar 

  69. Guo Z, Shu Y, Zhou H et al (2015) Radiogenomics helps to achieve personalized therapy by evaluating patient responses to radiation treatment. Carcinogenesis 36:307–317

    Article  CAS  Google Scholar 

  70. Rizzo S, Petrella F, Buscarino V et al (2016) CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer. Eur Radiol 26:32–42

    Article  Google Scholar 

  71. Yamamoto S, Korn RL, Oklu R et al (2014) ALK molecular phenotype in non-small cell lung cancer: CT radiogenomic characterization. Radiology 272:568–576

    Article  Google Scholar 

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Acknowledgements

The authors thank Prof. Hans Ulrich Kauczor for his scientific contribution to improve this manuscript.

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Authors and Affiliations

  1. LABIMED – Medical Imaging Research Lab, Department of Radiology, Pavilhão, Pereira Filho Hospital, Irmandade Santa Casa de Misericórdia de Porto Alegre, Av. Independência, 75, Porto Alegre, 90020-160, Brazil

    Bruno Hochhegger

  2. Department of Imaging, Pontifical Catholic University of Rio Grande do Sul, Av. Ipiranga, 6681 - Partenon, Porto Alegre, RS, 90619-900, Brazil

    Bruno Hochhegger & Guilherme Watte

  3. Medical Imaging Research Laboratory, Federal University of Health Sciences of Porto Alegre, R. Sarmento Leite, 245, Porto Alegre, Rio Grande Do Sul, 90050-170, Brazil

    Matheus Zanon, Stephan Altmayer, Gabriel S. Pacini, Fernanda Balbinot & Guilherme Watte

  4. Department of Radiology, Irmandade da Santa Casa de Misericordia de Porto Alegre, Av. Independência, 75, Porto Alegre, 90035-072, Brazil

    Martina Z. Francisco, Ruhana Dalla Costa & Marcelo C. Barros

  5. Ribeirao Preto Medical School, Av. Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, São Paulo, 14049-900, Brazil

    Marcel Koenigkam Santos

  6. Radiology, Liverpool Heart and Chest Hospital, Thomas Dr, Liverpool, L14 3PE, UK

    Diana Penha

  7. Central Manchester University Hospitals NHS Foundation Trust, Manchester Royal Infirmary, Oxford Road, Manchester, M13 9WL, UK

    Klaus Irion

  8. Federal University of Rio de Janeiro, Av. Carlos Chagas Filho, 373, Rio De Janeiro, 21941-902, Brazil

    Edson Marchiori

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  1. Bruno Hochhegger
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  2. Matheus Zanon
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  3. Stephan Altmayer
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Correspondence to Bruno Hochhegger.

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Hochhegger, B., Zanon, M., Altmayer, S. et al. Advances in Imaging and Automated Quantification of Malignant Pulmonary Diseases: A State-of-the-Art Review. Lung 196, 633–642 (2018). https://doi.org/10.1007/s00408-018-0156-0

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