Skip to main content

Advertisement

Springer Nature Link
Log in

Radiomics and deep learning in lung cancer

  • Review Article
  • Published:
Strahlentherapie und Onkologie Aims and scope Submit manuscript

Abstract

Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis–treatment–follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68:7–30

    Article  Google Scholar 

  2. Hawkins S, Wang H, Liu Y, Garcia A, Stringfield O, Krewer H et al (2016) Predicting malignant nodules from screening CT scans. J Thorac Oncol 11(12):2120–2128

    Article  PubMed  PubMed Central  Google Scholar 

  3. Avanzo M, Stancanello J, El Naqa I (2017) Beyond imaging: The promise of radiomics. Phys Med 38:122-139

    Article  PubMed  Google Scholar 

  4. Castiglioni I, Gallivanone F, Soda P, et al (2019) AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics. Eur J Nucl Med Mol Imaging 46(13):2673–2699. https://doi.org/10.1007/s00259-019-04414-4

    Article  PubMed  Google Scholar 

  5. Hassani C, Varghese BA, Nieva J, Duddalwar V (2019) Radiomics in pulmonary lesion imaging. AJR Am J Roentgenol 212:497–504

    Article  PubMed  Google Scholar 

  6. Nwogu I, Corso JJ (2008) Exploratory identification of image-based biomarkers for solid mass pulmonary tumors. Med Image Comput Comput Assist Interv 11:612–619

    PubMed  Google Scholar 

  7. Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA (2010) Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 10:137–143

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ganeshan B, Goh V, Mandeville HC, Ng QS, Hoskin PJ, Miles KA (2013) Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 266:326–336

    Article  PubMed  Google Scholar 

  9. Ciompi F, Chung K, van Riel SJ, Setio AAA, Gerke PK, Jacobs C et al (2017) Towards automatic pulmonary nodule management in lung cancer screening with deep learning. SciRep 7:46479

    Article  PubMed  PubMed Central  Google Scholar 

  10. D’Arnese E, di Donato GW, del Sozzo E, Santambrogio MD (2019) Towards an automatic imaging biopsy of non-small cell lung cancer. 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp 1–4

    Google Scholar 

  11. Hawkins SH, Korecki JN, Balagurunathan Y, Gu Y, Kumar V, Basu S et al (2014) Predicting outcomes of nonsmall cell lung cancer using CT image features. IEEE Access 2:1418–1426

    Article  Google Scholar 

  12. Shi L, He Y, Yuan Z et al (2018) Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer. Technol Cancer Res Treat 17:1533033818782788. https://doi.org/10.1177/1533033818782788

    Article  PubMed  PubMed Central  Google Scholar 

  13. Lian C, Ruan S, Denoeux T, Jardin F, Vera P (2016) Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction. Med Image Anal 32:257–268

    Article  PubMed  Google Scholar 

  14. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087

    Article  PubMed  PubMed Central  Google Scholar 

  15. van Timmeren JE, Leijenaar RT, van Elmpt W, Lambin P (2016) Interchangeability of a radiomic signature between conventional and weekly cone beam computed tomography allowing response prediction in non-small cell lung cancer. Int J Radiat Oncol Biol Phys 96:S193

    Article  Google Scholar 

  16. Fave X, Mackin D, Yang J, Zhang J, Fried D, Balter P et al (2015) Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? Med Phys 42:6784–6797

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zhang T, Yuan M, Zhong Y, Zhang YD, Li H, Wu JF et al (2019) Differentiation of focal organising pneumonia and peripheral adenocarcinoma in solid lung lesions using thin-section CT-based radiomics. Clin Radiol 74:78.e23–78.e30

    Article  Google Scholar 

  18. Balagurunathan Y, Schabath MB, Wang H, Liu Y, Gillies RJ (2019) Quantitative imaging features improve discrimination of malignancy in pulmonary nodules. Sci Rep. https://doi.org/10.1038/s41598-019-44562-z

    Article  PubMed  PubMed Central  Google Scholar 

  19. Petkovska I, Shah SK, McNitt-Gray MF, Goldin JG, Brown MS, Kim HJ et al (2006) Pulmonary nodule characterization: a comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps. Eur J Radiol 59:244–252

    Article  PubMed  PubMed Central  Google Scholar 

  20. Chen CH, Chang CK, Tu CY, Liao WC, Wu BR, Chou KT et al (2018) Radiomic features analysis in computed tomography images of lung nodule classification. PLoS ONE 13:e192002

    Article  PubMed  PubMed Central  Google Scholar 

  21. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    Article  PubMed  PubMed Central  Google Scholar 

  22. da Silva GLF, Valente TLA, Silva AC, de Paiva AC, Gattass M (2018) Convolutional neural network-based PSO for lung nodule false positive reduction on CT images. Comput Methods Programs Biomed 162:109–118

    Article  PubMed  Google Scholar 

  23. Feng B, Chen X, Chen Y, Li Z, Hao Y, Zhang C, Li R, Liao Y, Zhang X, Huang Y, Long W (2019) Differentiating minimally invasive and invasive adenocarcinomas in patients with solitary sub-solid pulmonary nodules with a radiomics nomogram. Clin Radiol 74:570.e1–570.e11. https://doi.org/10.1016/j.crad.2019.03.018

    Article  CAS  Google Scholar 

  24. Choi W, Oh JH, Riyahi S, Liu CJ, Jiang F, Chen W et al (2018) Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med Phys 45:1537–1549

    Article  PubMed  PubMed Central  Google Scholar 

  25. Weng Q, Zhou L, Wang H, Hui J, Chen M, Pang P et al (2019) A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules. Clin Radiol 74:933–943

    Article  PubMed  Google Scholar 

  26. Wu W, Parmar C, Grossmann P, Quackenbush J, Lambin P, Bussink J et al (2016) Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol 6:71

    PubMed  PubMed Central  Google Scholar 

  27. Coroller TP, Agrawal V, Narayan V, Hou Y, Grossmann P, Lee SW et al (2016) Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol 119(3):480–486

    Article  PubMed  PubMed Central  Google Scholar 

  28. Huynh E, Coroller TP, Narayan V, Agrawal V, Hou Y, Romano J et al (2016) CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Radiother Oncol 120(2):258–266

    Article  PubMed  Google Scholar 

  29. Rios Velazquez E, Aerts HJWL, Gu Y, Goldgof DB, De Ruysscher D, Dekker A et al (2012) A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists’ delineations and with the surgical specimen. Radiother Oncol 105:167–173

    Article  PubMed  Google Scholar 

  30. Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak RH et al (2014) Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 9:e102107

    Article  PubMed  PubMed Central  Google Scholar 

  31. Elter M, Horsch A (2009) CADx of mammographic masses and clustered microcalcifications: a review. Med Phys 36:2052–2068

    Article  PubMed  Google Scholar 

  32. Liu Y, Kim J, Balagurunathan Y, Li Q, Garcia AL, Stringfield O et al (2016) Radiomic features are associated with EGFR mutation status in lung adenocarcinomas. Clin Lung Cancer 17(5):441–448.e6

    Article  PubMed  PubMed Central  Google Scholar 

  33. Tan Y, Schwartz LH, Zhao B (2013) Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field. Med Phys 40:43502

    Article  PubMed  PubMed Central  Google Scholar 

  34. Oliver JA, Budzevich M, Zhang GG, Dilling TJ, Latifi K, Moros EG (2015) Variability of image features computed from conventional and respiratory-gated PET/CT images of lung cancer. Transl Oncol 8:524:534

    Google Scholar 

  35. Zaidi H, El Naqa I (2010) PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. Eur J Nucl Med Mol Imaging 37:2165–2187

    Article  PubMed  Google Scholar 

  36. Soufi M, Kamali-Asl A, Geramifar P, Rahmim A (2017) A novel framework for automated segmentation and labeling of homogeneous versus heterogeneous lung tumors in [(18)F]FDG-PET imaging. Mol Imaging Biol 19:456–468

    Article  PubMed  Google Scholar 

  37. Bug D, Feuerhake F, Oswald E, Schuler J, Merhof D (2019) Semi-automated analysis of digital whole slides from humanized lung-cancer xenograft models for checkpoint inhibitor response prediction. Oncotarget 10:4587–4597

    Article  PubMed  PubMed Central  Google Scholar 

  38. Lustberg T, van Soest J, Gooding M, Peressutti D, Aljabar P, van der Stoep J et al (2018) Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol 126:312–317

    Article  PubMed  Google Scholar 

  39. Ait Skourt B, El Hassani A, Majda A (2018) Lung CT image segmentation using deep neural networks. Procedia Comput Sci 127:109–113

    Article  Google Scholar 

  40. Zhong Z, Kim Y, Zhou L, Plichta K, Allen B, Buatti J et al (2018) 3D fully convolutional networks for co-segmentation of tumors on PET-CT images. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp 228–231

    Google Scholar 

  41. Ferreira Junior JR, Koenigkam-Santos M, Cipriano FEG, Fabro AT, Azevedo-Marques PM (2018) Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput Programs Biomed 159:23–30

    Article  Google Scholar 

  42. Song SH, Park H, Lee G, Lee HY, Sohn I, Kim HS et al (2017) Imaging phenotyping using radiomics to predict micropapillary pattern within lung adenocarcinoma. J Thorac Oncol 12:624–632

    Article  PubMed  Google Scholar 

  43. Zhang L, Chen B, Liu X, Song J, Fang M, Hu C et al (2018) Quantitative biomarkers for prediction of epidermal growth factor receptor mutation in non-small cell lung cancer. Transl Oncol 11:94–101

    Article  PubMed  Google Scholar 

  44. Li S, Ding C, Zhang H, Song J, Wu L (2019) Radiomics for the prediction of EGFR mutation subtypes in non-small cell lung cancer. Med Phys 46(10):4545–4552

    Article  PubMed  Google Scholar 

  45. Tu W, Sun G, Fan L, Wang Y, Xia Y, Guan Y et al (2019) Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology. Lung Cancer 132:28–35

    Article  PubMed  Google Scholar 

  46. Yip SSF, Kim J, Coroller TP, Parmar C, Velazquez ER, Huynh E et al (2017) Associations between somatic mutations and metabolic imaging phenotypes in non-small cell lung cancer. J Nucl Med 58:569–576

    Article  PubMed  PubMed Central  Google Scholar 

  47. Bodalal Z, Trebeschi S, Nguyen-Kim TDL, Schats W, Beets-Tan R (2019) Radiogenomics: bridging imaging and genomics. Abdom Radiol (NY) 44:1960–1984

    Article  Google Scholar 

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

    Article  Google Scholar 

  49. Grossmann P, Stringfield O, El-Hachem N, Bui MM, Rios Velazquez E, Parmar C et al (2017) Defining the biological basis of radiomic phenotypes in lung cancer. Elife. https://doi.org/10.7554/eLife.23421

    Article  PubMed  PubMed Central  Google Scholar 

  50. Hosny A, Parmar C, Coroller TP, Grossmann P, Zeleznik R, Kumar A et al (2018) Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med 15:e1002711

    Article  PubMed  PubMed Central  Google Scholar 

  51. Lafata KJ, Hong JC, Geng R, Ackerson BG, Liu JG, Zhou Z et al (2019) Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy. Phys Med Biol. https://doi.org/10.1088/1361-6560/aaf5a5

    Article  PubMed  Google Scholar 

  52. Zhang Y, Oikonomou A, Wong A, Haider MA, Khalvati F (2017) Radiomics-based prognosis analysis for non-small cell lung cancer. Sci Rep 7:46349

    Article  PubMed  PubMed Central  Google Scholar 

  53. Oikonomou A, Khalvati F, Tyrrell PN, Haider MA, Tarique U, Jimenez-Juan L et al (2018) Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy. Sci Rep. https://doi.org/10.1038/s41598-018-22357-y

    Article  PubMed  PubMed Central  Google Scholar 

  54. Dissaux G, Visvikis D, Da-Ano R, Pradier O, Chajon E, Barillot I et al (2019) Pre-treatment (18)F-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. https://doi.org/10.2967/jnumed.119.228106

    Article  PubMed  Google Scholar 

  55. Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, Hermann G et al (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114:345–350

    Article  PubMed  PubMed Central  Google Scholar 

  56. Dou TH, Coroller TP, van Griethuysen JJM, Mak RH, Aerts HJWL (2018) Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC. PLoS ONE 13:e206108

    Article  PubMed  PubMed Central  Google Scholar 

  57. Hao H, Zhou Z, Li S, Maquilan G, Folkert MR, Iyengar P et al (2018) Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer. Phys Med Biol. https://doi.org/10.1088/1361-6560/aabb5e

    Article  PubMed  PubMed Central  Google Scholar 

  58. Ohri N, Duan F, Snyder BS, Wei B, Machtay M, Alavi A et al (2016) Pretreatment 18F-FDG PET textural features in locally advanced non-small cell lung cancer: secondary analysis of ACRIN 6668/RTOG 0235. J Nucl Med 57:842–848

    Article  PubMed  PubMed Central  Google Scholar 

  59. Xu Y, Hosny A, Zeleznik R, Parmar C, Coroller T, Franco I et al (2019) Deep learning predicts lung cancer treatment response from serial medical imaging. Clin Cancer Res 25:3266–3275

    Article  PubMed  PubMed Central  Google Scholar 

  60. Paul R, Hawkins SH, Balagurunathan Y, Schabath MB, Gillies RJ, Hall LO et al (2016) Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography 2:388–395

    Article  PubMed  PubMed Central  Google Scholar 

  61. Astaraki M, Wang C, Buizza G, Toma-Dasu I, Lazzeroni M, Smedby O (2019) Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method. Phys Med 60:58–65

    Article  PubMed  Google Scholar 

  62. Khorrami M, Khunger M, Zagouras A, Patil P, Thawani R, Bera K et al (2019) Combination of peri- and intratumoral radiomic features on baseline CT scans predicts response to chemotherapy in lung adenocarcinoma. Radiol Artif Intell 1:e180012

    Article  PubMed  PubMed Central  Google Scholar 

  63. Aerts HJ (2016) The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol 2(12):1636

    Article  PubMed  Google Scholar 

  64. Jiang M, Sun D, Guo Y, Guo Y, Xiao J, Wang L et al (2019) Assessing PD-L1 expression level by radiomic features from PET/CT in nonsmall cell lung cancer patients: an initial result. Acad Radiol 27(2):171–179

    Article  PubMed  Google Scholar 

  65. Mattonen SA, Palma DA, Haasbeek CJ, Senan S, Ward AD (2014) Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer. Med Phys 41:33502

    Article  PubMed  Google Scholar 

  66. Yu W, Tang C, Hobbs BP, Li X, Koay EJ, Wistuba II et al (2018) Development and validation of a predictive radiomics model for clinical outcomes in stage I non-small cell lung cancer. Int J Radiat Oncol Biol Phys 102:1090–1097

    Article  PubMed  Google Scholar 

  67. Moran A, Daly ME, Yip SSF, Yamamoto T (2017) Radiomics-based assessment of radiation-induced lung injury after stereotactic body radiotherapy. Clin Lung Cancer 18:e425–e431

    Article  PubMed  Google Scholar 

  68. Cunliffe A, Armato SG 3rd, Castillo R, Pham N, Guerrero T, Al-Hallaq HA (2015) Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. Int J Radiat Oncol Biol Phys 91:1048–1056

    Article  PubMed  PubMed Central  Google Scholar 

  69. Krafft SP, Rao A, Stingo F, Briere TM, Court LE, Liao Z et al (2018) The utility of quantitative CT radiomics features for improved prediction of radiation pneumonitis. Med Phys 45:5317–5324

    Article  PubMed  Google Scholar 

  70. Colen RR, Fujii T, Bilen MA, Kotrotsou A, Abrol S, Hess KR et al (2018) Radiomics to predict immunotherapy-induced pneumonitis: proof of concept. Invest New Drugs 36:601–607

    Article  PubMed  Google Scholar 

  71. Liang B, Yan H, Tian Y, Chen X, Yan L, Zhang T et al (2019) Dosiomics: extracting 3D spatial features from dose distribution to predict incidence of radiation pneumonitis. Front Oncol 9:269

    Article  PubMed  PubMed Central  Google Scholar 

  72. Avanzo M, Trovo M, Furlan C, Barresi L, Linda A, Stancanello J, Andreon L, Minatel E, Bazzocchi M, Trovo MG, Capra E (2015) Normal tissue complication probability models for severe acute radiological lung injury after radiotherapy for lung cancer. Phys Med 31(1):1–8

    Article  PubMed  Google Scholar 

  73. Mattonen SA, Palma DA, Johnson C, Louie AV, Landis M, Rodrigues G et al (2016) Detection of local cancer recurrence after stereotactic ablative radiation therapy for lung cancer: physician performance versus radiomic assessment. Int J Radiat Oncol Biol Phys 94:1121–1128

    Article  PubMed  Google Scholar 

  74. Fried DV, Mawlawi O, Zhang L, Fave X, Zhou S, Ibbott G et al (2016) Stage III non-small cell lung cancer: prognostic value of FDG PET quantitative imaging features combined with clinical prognostic factors. Radiology 278:214–222

    Article  PubMed  Google Scholar 

  75. Aerts HJ, Grossmann P, Tan Y, Oxnard GG, Rizvi N, Schwartz LH et al (2016) Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC. Sci Rep 6:33860

    Article  PubMed  PubMed Central  Google Scholar 

  76. Fave X, Zhang L, Yang J, Mackin D, Balter P, Gomez D et al (2017) Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep. https://doi.org/10.1038/s41598-017-00665-z

    Article  PubMed  PubMed Central  Google Scholar 

  77. Shi L, Rong Y, Daly M, Dyer BA, Benedict S, Qiu J et al (2019) Cone-beam computed tomography-based delta-radiomics for early response assessment in radiotherapy for locally advanced lung cancer. Phys Med Biol. https://doi.org/10.1088/1361-6560/ab3247

    Article  PubMed  PubMed Central  Google Scholar 

  78. van Timmeren JE, Leijenaar RTH, van Elmpt W, Reymen B, Lambin P (2017) Feature selection methodology for longitudinal cone-beam CT radiomics. Acta Oncol 56:1537–1543

    Article  PubMed  Google Scholar 

  79. van Timmeren JE, van Elmpt W, Leijenaar RTH, Reymen B, Monshouwer R, Bussink J et al (2019) 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 136:78–85

    Article  PubMed  PubMed Central  Google Scholar 

  80. Du Q, Baine M, Bavitz K, McAllister J, Liang X, Yu H et al (2019) Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction. PLoS ONE 14:e216480

    Article  PubMed  PubMed Central  Google Scholar 

  81. Avanzo M, Barbiero S, Trovo M, Bissonnette JP, Jena R, Stancanello J et al (2017) Voxel-by-voxel correlation between radiologically radiation induced lung injury and dose after image-guided, intensity modulated radiotherapy for lung tumors. Phys Med 42:150–156

    Article  PubMed  Google Scholar 

  82. Deist TM, Dankers FJWM, Ojha P, Scott Marshall M, Janssen T, Faivre-Finn C et al (2020) Distributed learning on 20 000+ lung cancer patients—the Personal Health Train. Radiother Oncol 144:189–200

    Article  PubMed  Google Scholar 

  83. Khorrami M, Bera K, Leo P, Vaidya P, Patil P, Thawani R et al (2020) Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: multi-site study. Lung Cancer 142:90–97

    Article  PubMed  Google Scholar 

  84. Leijenaar RT, Carvalho S, Velazquez ER, van Elmpt WJ, Parmar C, Hoekstra OS et al (2013) Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol 52:1391–1397

    Article  PubMed  Google Scholar 

  85. van Timmeren JE, Carvalho S, Leijenaar RTH, Troost EGC, van Elmpt W, de Ruysscher D et al (2019) 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 14:e217536

    Article  PubMed  PubMed Central  Google Scholar 

  86. Jia X, Ren L, Cai J (2020) Clinical implementation of AI technologies will require interpretable AI models. Med Phys 47:1–4

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy

    Michele Avanzo, Giovanni Pirrone & Giovanna Sartor

  2. Guerbet SA, Villepinte, France

    Joseph Stancanello

Authors
  1. Michele Avanzo
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Joseph Stancanello
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Giovanni Pirrone
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Giovanna Sartor
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Michele Avanzo.

Ethics declarations

Conflict of interest

J. Stancanello is employed by the company Guerbet SA. M. Avanzo, G. Pirrone and G. Sartor declare that they have no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Avanzo, M., Stancanello, J., Pirrone, G. et al. Radiomics and deep learning in lung cancer. Strahlenther Onkol 196, 879–887 (2020). https://doi.org/10.1007/s00066-020-01625-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00066-020-01625-9

Keywords

Profiles

  1. Michele Avanzo View author profile