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Exploring the potential of Radiomics in identification and treatment of lung cancer: A systematic evaluation

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Abstract

Lung cancer is one of the most serious and life-threatening diseases in the world. Imaging modalities like computed tomography (CT) and Positron emission tomography (PET) play a crucial role in cancer diagnosis. Radiomics is an emerging field in medical imaging that uses advanced computational algorithms to extract quantitative features from medical images. Machine learning makes radiomics method of cancer diagnosis easier and more efficient by automating the process of feature selection and classification, which can save time and reduce the risk of human error in the diagnosis. It has the potential to revolutionize cancer detection by providing clinicians with valuable insights into tumour biology that can help in clinical decision-making and improve patient care outcomes. In this review paper, we primarily summarize the workflow of radiomics studies in the context of lung cancer and discussed the practical uses of radiomics in lung cancer, such as malignant tumour identification, classification of histologic subtypes, identification of tumour genotypes, and prediction of treatment response. Additionally, the paper addresses the key challenges associated with the clinical transition of radiomics, the limitations of current approaches, and potential future directions in this field.

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References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660

    Article  Google Scholar 

  2. Chen Z, Fillmore CM, Hammerman PS, Kim CF, Wong KK (2014) Non-small-cell lung cancers: a heterogeneous set of diseases. Nat Rev Cancer 14(8):535–546. https://doi.org/10.1038/nrc3775

    Article  Google Scholar 

  3. American Cancer Society (2023) Lung Cancer Survival Rates. https://www.cancer.org/cancer/lung-cancer/detection-diagnosis-staging/survival-rates.html. Accessed 10 Feb 2023

  4. De Wever W, Coolen J, Verschakelen J (2011) Imaging techniques in lung cancer Breathe 7:338–346. https://doi.org/10.1183/20734735.022110

    Article  Google Scholar 

  5. Tam AL et al (2016) Image-Guided Biopsy in the Era of Personalized Cancer Care. Proceed Soc Intervent Radiol Res Consensus Panel 27(1):8–19. https://doi.org/10.1016/j.jvir.2015.10.019

    Article  Google Scholar 

  6. Saif MW, Tzannou I, Makrilia N, Syrigos K (2010) Role and cost effectiveness of PET/CT in management of patients with cancer. Yale J Biol Med 83(2):53–65. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2892773/. Accessed 10 Feb 2023

  7. Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J (2019) The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics 9(5):1303–1322. https://doi.org/10.7150/thno.30309

    Article  Google Scholar 

  8. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images Are More than Pictures. They Are Data Radiology 278(2):563–577. https://doi.org/10.1148/radiol.2015151169

    Article  Google Scholar 

  9. Aerts, H, Velazquez, ER, Leijenaar, RTH, Parmar, C, Grossmann, P, Carvalho, S, Bussink, J, Monshouwer, R, Haibe-Kains, B, Rietveld, D, Hoebers, F, Rietbergen, MM, Leemans, CR, Dekker, A, Quackenbush, J, Gillies, RJ, Lambin, P (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5 (4006). https://doi.org/10.1038/ncomms5006

  10. Kirienko M, Cozzi L, Antunovic L et al (2018) Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging 45:207–217. https://doi.org/10.1007/s00259-017-3837-7

    Article  Google Scholar 

  11. Lee G, Lee HY, Park H, Schiebler ML, van Beek EJR, Ohno Y, Seo JB, Leung A (2017) Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art. Eur J Radiol 86:297–307. https://doi.org/10.1016/j.ejrad.2016.09.005

    Article  Google Scholar 

  12. Gong Jw, Zhang Z, Luo Ty et al (2022) Combined model of radiomics, clinical, and imaging features for differentiating focal pneumonia-like lung cancer from pulmonary inflammatory lesions: an exploratory study. BMC Med Imaging 22(98). https://doi.org/10.1186/s12880-022-00822-5

  13. Haider SP, Burtness B, Yarbrough WG, Payabvash S (2020) Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas. Cancers Head Neck 5(1). https://doi.org/10.1186/s41199-020-00053-7

  14. Li Y, Wu X, Yang P, Jiang G, Luo Y (2022) Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis. Genom Proteom Bioinf 20(5). https://doi.org/10.1016/j.gpb.2022.11.003

  15. Recommendation United States Preventive Services Taskforce (2021). https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/lung-cancer-screening. Accessed 10 Feb 2023

  16. Jonas DE, Reuland DS, Reddy SM et al (2021) Screening for Lung Cancer With Low-Dose Computed Tomography: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 325(10):971–987. https://doi.org/10.1001/jama.2021.0377

    Article  Google Scholar 

  17. Park S, Lee SM, Do KH, Lee JG, Bae W, Park H, Jung KH, Seo JB (2019) Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer. Korean J Radiol 20(10):1431–1440. https://doi.org/10.3348/kjr.2019.0212

    Article  Google Scholar 

  18. Yang J, Zhang L, Fave XJ, Fried DV, Stingo FC, Ng CS, Court LE (2016) Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors. Comput Med Imaging Graph 48:1–8. https://doi.org/10.1016/j.compmedimag.2015.12.001

    Article  Google Scholar 

  19. Takamasa Mitsuyoshi M, Nakamura TM, Shintani T, Hirashima H (2020) Comparison of radiomic features in diagnostic CT images with and without contrast enhancement in the delayed phase for NSCLC patients. Physica Med 69:176–182. https://doi.org/10.1016/j.ejmp.2019.12.019

    Article  Google Scholar 

  20. 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. Translational Oncol 8(6):524–534. https://doi.org/10.1016/j.tranon.2015.11.013

    Article  Google Scholar 

  21. Lo P, Young S, Kim HJ, Brown MS, McNitt-Gray MF (2016) Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features. Med Phys 43(8):4854–4865. https://doi.org/10.1118/1.4954845

    Article  Google Scholar 

  22. Abhishek Midya J, Chakraborty MG, Richard, and A. L. Simpson (2018) Influence of CT acquisition and reconstruction parameters on radiomic feature reproducibility. J Med Imaging 5(1):1–1. https://doi.org/10.1117/1.jmi.5.1.011020

    Article  Google Scholar 

  23. Zhao B, Tan Y, Tsai W-Y, Schwartz LB, Lu L (2014) Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study. Translat Oncol 7(1):88–93. https://doi.org/10.1593/tlo.13865

    Article  Google Scholar 

  24. Weber NM, Koo CW, Lifeng Yu, Bartholmai BJ, Halaweish AF, McCollough CH, Fletcher JG (2019) Breathe New Life Into Your Chest CT Exams: Using Advanced Acquisition and Postprocessing Techniques. Curr Probl Diagn Radiol 48(2):152–160. https://doi.org/10.1067/j.cpradiol.2018.10.003

    Article  Google Scholar 

  25. Lehmann TM, Gonner C, Spitzer K (1999) Survey: interpolation methods in medical image processing. IEEE Trans Med Imagin 18(11):1049–1075. https://doi.org/10.1109/42.816070

    Article  Google Scholar 

  26. Kociołek M, Strzelecki M, Obuchowicz R (2020) Does image normalization and intensity resolution impact texture classification? Comput Med Imaging Graph 81:101716. https://doi.org/10.1016/j.compmedimag.2020.101716

    Article  Google Scholar 

  27. Sun X, Shi L, Luo Y et al (2015) Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions. BioMed Eng OnLine 14:73. https://doi.org/10.1186/s12938-015-0064-y

    Article  Google Scholar 

  28. R. Zhu and Y. Wang (2012) Application of Improved Median Filter on Image Processing. J Comput 7(4). https://doi.org/10.4304/jcp.7.4.838-841

  29. M. Abdel-Basset, A. E. Fakhry, I. El-henawy, T. Qiu, and A. K. Sangaiah (2017) Feature and Intensity Based Medical Image Registration Using Particle Swarm Optimization. J Med Syst 41(12). https://doi.org/10.1007/s10916-017-0846-9

  30. Rister B, Horowitz MA, Rubin DL (2017) Volumetric Image Registration From Invariant Keypoints. IEEE Trans Image Process 26(10):4900–4910. https://doi.org/10.1109/tip.2017.2722689

    Article  MathSciNet  Google Scholar 

  31. Zheng Q, Wang Q, Ba X, Liu S, Nan J, Zhang S (2021) A Medical Image Registration Method Based on Progressive Images. Comput Math Methods Med 2021:1–9. https://doi.org/10.1155/2021/4504306

    Article  Google Scholar 

  32. Maolood IY, Al-Salhi YEA, Lu S (2018) Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm. Open Med 13(1):374–383. https://doi.org/10.1515/med-2018-0056

    Article  Google Scholar 

  33. Shrivastava N, Bharti J (2020) Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review. Int J Image Graph 20(3):2050018. https://doi.org/10.1142/s0219467820500187

    Article  Google Scholar 

  34. Hemalatha RJ, Thamizhvani TR, Dhivya AJA, Joseph JE, Babu B, Chandrasekaran R (2018) Active Contour Based Segmentation Techniques for Medical Image Analysis. Med Biol Image Anal. https://doi.org/10.5772/intechopen.74576

    Article  Google Scholar 

  35. Yang Y, Hou X, Ren H (2022) Efficient active contour model for medical image segmentation and correction based on edge and region information. Expert Syst Appl 194:116436. https://doi.org/10.1016/j.eswa.2021.116436

  36. Chen X, Williams BM, Vallabhaneni SR, Czanner G, Williams R, Zheng Y (2019) Learning Active Contour Models for Medical Image Segmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.01190

  37. Chen C-H, Chang C-K, Tu C-Y, Liao W-C, Wu B-R, Chou K-T et al (2018) Radiomic features analysis in computed tomography images of lung nodule classification. PLoS ONE 13(2):e0192002. https://doi.org/10.1371/journal.pone.0192002

  38. Ganeshan B, Miles KA (2013) Quantifying tumour heterogeneity with CT. Cancer Imaging 13(1):140–149. https://doi.org/10.1102/1470-7330.2013.0015

    Article  Google Scholar 

  39. 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(7):e102107. https://doi.org/10.1371/journal.pone.0102107

  40. Primakov SP, Ibrahim A, van Timmeren JE et al (2022) Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nat Commun 13:3423. https://doi.org/10.1038/s41467-022-30841-3

    Article  Google Scholar 

  41. Liu X, Li K.-W, Yang R, Geng L-S (2021) Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy. Front Oncol 11. https://doi.org/10.3389/fonc.2021.717039

  42. Luo D, Zeng W, Chen J, Tang W (2021) Deep Learning for Automatic Image Segmentation in Stomatology and Its Clinical Application. Front Med Technol 3. https://doi.org/10.3389/fmedt.2021.767836

  43. Said Y, Alsheikhy AA, Shawly T, Lahza H (2023) Medical Images Segmentation for Lung Cancer Diagnosis Based on Deep Learning Architectures. Diagnostic 13(3):546–546. https://doi.org/10.3390/diagnostics13030546

    Article  Google Scholar 

  44. Joskowicz L, Cohen D, Caplan N, Sosna J (2018) Inter-observer variability of manual contour delineation of structures in CT. Eur Radiol 29(3):1391–1399. https://doi.org/10.1007/s00330-018-5695-5

    Article  Google Scholar 

  45. Zou KH, Warfield SK, Bharatha A, Tempany CMC, Kaus MR, Haker SJ, Wells III WM, Jolesz FA, Kikinis R (2014) Statistical Validation of Image Segmentation Quality Based on a Spatial Overlap Index. Acad Radiol 11(2):178–189. https://doi.org/10.1016/S1076-6332(03)00671-8

    Article  Google Scholar 

  46. Aydin OU, Taha AA, Hilbert A et al (2021) On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking. Eur Radiol Exp 5(4). https://doi.org/10.1186/s41747-020-00200-2

  47. Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15. https://doi.org/10.1186/s12880-015-0068-x

  48. Court LE, Fave X, Mackin D, Lee J, Yang J, Zhang L (2016) Computational resources for radiomics. Translat Cancer Res 5(4):340–348. https://doi.org/10.21037/tcr.2016.06.17

    Article  Google Scholar 

  49. Bianconi F, Fravolini ML, Bello-Cerezo R, Minestrini M, Scialpi M, Palumbo B (2018) Evaluation of Shape and Textural Features from CT as Prognostic Biomarkers in Non-small Cell Lung Cancer. Anticancer Res 38(4):2155–2160. https://doi.org/10.21873/anticanres.12456

    Article  Google Scholar 

  50. Wu H, Sun T, Wang J et al (2013) Combination of Radiological and Gray Level Co-occurrence Matrix Textural Features Used to Distinguish Solitary Pulmonary Nodules by Computed Tomography. J Digit Imaging 26:797–802. https://doi.org/10.1007/s10278-012-9547-6

    Article  Google Scholar 

  51. Houseni M, Mahmoud M, Saad S, ElHussiny F, Shihab M (2021) Advanced intra-tumoural structural characterisation of hepatocellular carcinoma utilising FDG-PET/CT: a comparative study of radiomics and metabolic features in 3D and 2D. Pol J Radiol 86(1):64–73. https://doi.org/10.5114/pjr.2021.103239

    Article  Google Scholar 

  52. Khodabakhshi Z, Shayan Mostafaei H, Arabi MO, Shiri I, Zaidi H (2021) Non-small cell lung carcinoma histopathological subtype phenoty** using high-dimensional multinomial multiclass CT radiomics signature. Comput Biol Med 136:104752–104752. https://doi.org/10.1016/j.compbiomed.2021.104752

    Article  Google Scholar 

  53. Benmazou S, Merouani HF (2018) Wavelet based feature extraction method for breast cancer diagnosis. Int Conf Adv Technol Signal Image Process. https://doi.org/10.1109/atsip.2018.8364477

    Article  Google Scholar 

  54. Brandão L, Belfo FP, Silva A (2021) Wavelet-based cancer drug recommender system. Procedia Comput Sci 181:487–494. https://doi.org/10.1016/j.procs.2021.01.194

    Article  Google Scholar 

  55. Roberts T, Newell M, Auffermann W, Vidakovic B (2017) Wavelet-based scaling indices for breast cancer diagnostics. Stat Med. https://doi.org/10.1002/sim.7264

    Article  MathSciNet  Google Scholar 

  56. Mwangi B, Tian TS, Soares JC (2013) A Review of Feature Reduction Techniques in Neuroimaging. Neuroinformatics 12(2):229–244. https://doi.org/10.1007/s12021-013-9204-3

    Article  Google Scholar 

  57. Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M (2020) Benchmark for filter methods for feature selection in high-dimensional classification data. Comput Stat Data Anal 143:106839. https://doi.org/10.1016/j.csda.2019.106839

  58. Tibshirani R (2011) Regression shrinkage and selection via the lasso: a retrospective. J Royal Stat Soc Series B (Stat Methodol) 73(3):273–282. https://doi.org/10.1111/j.1467-9868.2011.00771.x

    Article  MathSciNet  Google Scholar 

  59. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J Royal Stat Soc Ser B Stat Methodol 67(2):301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x

    Article  MathSciNet  Google Scholar 

  60. Binczyk F, Prazuch W, Bozek P, Polanska J (2021) Radiomics and artificial intelligence in lung cancer screening. Translat Lung Cancer Res 10(2):1186–1199. https://doi.org/10.21037/tlcr-20-708

    Article  Google Scholar 

  61. Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L (2022) Deep learning with radiomics for disease diagnosis and treatment: Challenges and potential. Front Oncol 12:773840. https://doi.org/10.3389/fonc.2022.773840

  62. Varma S, Simon R (2006) Bias in Error Estimation When Using Cross-Validation for Model Selection. BMC Bioinf 7(1):91. https://doi.org/10.1186/1471-2105-7-91

    Article  Google Scholar 

  63. Hayes DF (2014) Biomarker validation and testing. Mol Oncol 9(5):960–966. https://doi.org/10.1016/j.molonc.2014.10.004

    Article  Google Scholar 

  64. Wang J et al (2016) Prediction of malignant and benign of lung tumor using a quantitative radiomic method. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 1272–1275. https://doi.org/10.1109/embc.2016.7590938

  65. Wu W, Pierce LA, Zhang Y et al (2019) Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study. Eur Radiol 29:6100–6108. https://doi.org/10.1007/s00330-019-06213-9

    Article  Google Scholar 

  66. Samuel H, Hua W, Ying L, Alberto G et al (2016) Predicting Malignant Nodules from Screening CT Scans. J Thorac Oncol 11(12):2120–2128. https://doi.org/10.1016/j.jtho.2016.07.002

    Article  Google Scholar 

  67. Rui** Z, Lei Z, Zhengting C, Wei J et al (2019) Potential feature exploration and model development based on 18F-FDG PET/CT images for differentiating benign and malignant lung lesions. Eur J Radiol 121:108735–108735. https://doi.org/10.1016/j.ejrad.2019.108735

    Article  Google Scholar 

  68. **e Y, **a Y, Zhang J et al (2019) Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT. IEEE Trans Med Imaging 38(4):991–1004. https://doi.org/10.1109/tmi.2018.2876510

    Article  Google Scholar 

  69. Herbst RS, Heymach JV, Lippman SM (2008) Lung cancer. N Engl J Med 359(13):1367–1380. https://doi.org/10.1056/NEJMra0802714

    Article  Google Scholar 

  70. Wu W, Parmar C, Grossmann P, Quackenbush J, Lambin P, Bussink J, Mak R, Aerts HJ (2016) Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology. Front Oncol 6:71. https://doi.org/10.3389/fonc.2016.00071

    Article  Google Scholar 

  71. Patil R, Mahadevaiah G, Dekker A (2016) An Approach Toward Automatic Classification of Tumor Histopathology of Non-Small Cell Lung Cancer Based on Radiomic Features. Tomography 2(4):374–377. https://doi.org/10.18383/j.tom.2016.00244

    Article  Google Scholar 

  72. Zhu X, Dong D, Chen Z et al (2018) Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol 28:2772–2778. https://doi.org/10.1007/s00330-017-5221-1

    Article  Google Scholar 

  73. Bashir U, Kawa B, Siddique M, Mak SM, Nair A, Mclean E, Bille A, Goh V, Cook G (2019) Non-invasive classification of non-small cell lung cancer: a comparison between random forest models utilising radiomic and semantic features. Br J Radiol 92(1099):20190159. https://doi.org/10.1259/bjr.20190159

    Article  Google Scholar 

  74. Sha X, Gong G, Qiu Q, Duan J, Li D, Yin Y (2019) Identifying pathological subtypes of non-small-cell lung cancer by using the radiomic features of 18F-fluorodeoxyglucose positron emission computed tomography. Transl Cancer Res 8(5):1741–1749. https://doi.org/10.21037/tcr.2019.08.20

    Article  Google Scholar 

  75. Hyun SH, Ahn MS, Koh YW, Lee SJ (2019) A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer. Clin Nucl Med 44(12):956–960. https://doi.org/10.1097/RLU.0000000000002810

    Article  Google Scholar 

  76. Liu J, Cui J-J, Liu F, Yuan Y, Guo F, Zhang G (2019) Multi-subtype classification model for non-small cell lung cancer based on radiomics: SLS model. Med Phys 46(7):3091–3100. https://doi.org/10.1002/mp.13551

    Article  Google Scholar 

  77. Chaunzwa TL, Hosny A, Xu Y et al (2021) Deep learning classification of lung cancer histology using CT images. Sci Rep 11:5471. https://doi.org/10.1038/s41598-021-84630-x

    Article  Google Scholar 

  78. Marentakis P, Karaiskos P, Kouloulias V et al (2021) Lung cancer histology classification from CT images based on radiomics and deep learning models. Med Biol Eng Comput 59:215–226. https://doi.org/10.1007/s11517-020-02302-w

    Article  Google Scholar 

  79. Yixian G, Qiong S, Mengmeng J, Yinglong G et al (2021) Histological Subtypes Classification of Lung Cancers on CT Images Using 3D Deep Learning and Radiomics. Acad Radiol 28(9):e258–e266. https://doi.org/10.1016/j.acra.2020.06.010

    Article  Google Scholar 

  80. Wang J, Zhong F, **ao F, Dong X, Long Y, Gan T, Li T, Liao M (2023) CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma. Front Oncol 13:1157891. https://doi.org/10.3389/fonc.2023.1157891

    Article  Google Scholar 

  81. Lu J, Ji X, Wang L, Jiang Y, Liu X, Ma Z, Ning Y, Dong J, Peng H, Sun F, Guo Z, Ji Y, **ng J, Lu Y, Lu D (2022) Machine Learning-Based Radiomics for Prediction of Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma. Dis Markers 2022:2056837. https://doi.org/10.1155/2022/2056837

    Article  Google Scholar 

  82. Le QH, Kha VT, Nguyen Y-C, Cheng SJ, Chen C (2021) Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer. Int J Mol Sci 22(17):9254–9254. https://doi.org/10.3390/ijms22179254

    Article  Google Scholar 

  83. **ao Z, Cai H, Wang Y, Cui R, Huo L, Lee EY, Liang Y, Li X, Hu Z, Chen L, Zhang N (2023) Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images. Quant Imaging Med Surg 13(3):1286–1299. https://doi.org/10.21037/qims-22-760

    Article  Google Scholar 

  84. Huang X, Sun Y, Tan M, Ma W, Gao P, Qi L, Lu J, Yang Y, Wang K, Chen W, ** L, Kuang K, Duan S, Li M (2022) Three-Dimensional Convolutional Neural Network-Based Prediction of Epidermal Growth Factor Receptor Expression Status in Patients With Non-Small Cell Lung Cancer. Front Oncol 12:772770. https://doi.org/10.3389/fonc.2022.772770

  85. Song L, Zhu Z, Mao L, Li X, Han W, Du H, Wu H, Song W, ** Z (2020) Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients. Front Oncol 10:369. https://doi.org/10.3389/fonc.2020.00369

    Article  Google Scholar 

  86. Tu W, Sun G, Fan L, Wang Y, **a Y, Guan Y, Li Q, Zhang D, Liu S, Li Z (2019) Radiomics signature: A potential and incremental predictor for EGFR mutation status in NSCLC patients, comparison with CT morphology. Lung Cancer 132:28–35. https://doi.org/10.1016/j.lungcan.2019.03.025

    Article  Google Scholar 

  87. Chang C, Sun X, Wang G, Yu H, Zhao W, Ge Y, Duan S, Qian X, Wang R, Lei B, Wang L, Liu L, Ruan M, Yan H, Liu C, Chen J, **e W (2021) A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma. Front Oncol 11:603882. https://doi.org/10.3389/fonc.2021.603882

  88. Wang S, Shi J, Ye Z, Dong D, Yu D, Zhou M, Liu Y, Gevaert O, Wang K, Zhu Y, Zhou H, Liu Z, Tian J (2019) Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. Eur Respir J 53(3):1800986. https://doi.org/10.1183/13993003.00986-2018

    Article  Google Scholar 

  89. Dong Y, Hou L, Yang W, Han J, Wang J, Qiang Y, Zhao J, Hou J, Song K, Ma Y, Kazihise NGF, Cui Y, Yang X (2021) Multi-channel multi-task deep learning for predicting EGFR and KRAS mutations of non-small cell lung cancer on CT images. Quant Imaging Med Surg 11(6):2354–2375. https://doi.org/10.21037/qims-20-600

    Article  Google Scholar 

  90. Yin G, Wang Z, Song Y, Li X, Chen Y, Zhu L, Su Q, Dai D, Xu W (2021) Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma. Front Oncol 11:709137. https://doi.org/10.3389/fonc.2021.709137

  91. Chen S, Han X, Tian G, Cao Y, Zheng X, Li X, Li Y (2022) Using stacked deep learning models based on PET/CT images and clinical data to predict EGFR mutations in lung cancer. Front Med (Lausanne) 9:1041034. https://doi.org/10.3389/fmed.2022.1041034

  92. Coroller TP, Agrawal V, Huynh E, Narayan V, Lee SW, Mak RH, Aerts HJWL (2017) Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC. J Thorac Oncol 12(3):467–476. https://doi.org/10.1016/j.jtho.2016.11.2226

    Article  Google Scholar 

  93. Kirienko M, Cozzi L, Antunovic L, Lozza L, Fogliata A, Voulaz E, Rossi A, Chiti A, Sollini M (2018) Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging 45(2):207–217. https://doi.org/10.1007/s00259-017-3837-7

    Article  Google Scholar 

  94. Zhang N, Liang R, Gensheimer MF, Guo M, Zhu H, Yu J, Diehn M, Loo BW Jr, Li R, Wu J (2020) Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer. Theranostics 10(25):11707–11718. https://doi.org/10.7150/thno.50565

    Article  Google Scholar 

  95. Vaidya P, Bera K, Gupta A, Wang X, Corredor G, Fu P, Beig N, Prasanna P, Patil P, Velu P, Rajiah P, Gilkeson R, Feldman M, Choi H, Velcheti V, Madabhushi A (2020) CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in Stage I, II resectable Non-Small Cell Lung Cancer: a retrospective multi-cohort study for outcome prediction. Lancet Digit Health 2(3):e116–e128. https://doi.org/10.1016/S2589-7500(20)30002-9

    Article  Google Scholar 

  96. Khorrami M, Khunger M, Zagouras A, Patil P, Thawani R, Bera K, Rajiah P, **fu Fu, Velcheti V, Madabhushi A (2019) Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma. Radiology 1(2):180012–180012. https://doi.org/10.1148/ryai.2019180012

    Article  Google Scholar 

  97. Mu W, Qi J, Lu H, Schabath M, Balagurunathan Y, Tunali I, Gillies RJ (2018) Radiomic biomarkers from PET/CT multi-modality fusion images for the prediction of immunotherapy response in advanced non-small cell lung cancer patients. Medical Imaging 2018: Comput Aide Diagnosis https://doi.org/10.1117/12.2293376

  98. Jain P, Khorrami M, Gupta A, Rajiah P, Bera K, Viswanathan VS, Fu P, Dowlati A, Madabhushi A (2021) Novel Non-Invasive Radiomic Signature on CT Scans Predicts Response to Platinum-Based Chemotherapy and Is Prognostic of Overall Survival in Small Cell Lung Cancer. Front Oncol 11:744724. https://doi.org/10.3389/fonc.2021.744724

  99. Yang F, Zhang J, Zhou L, **a W, Zhang R, Wei H, Feng J, Zhao X, Jian J, Gao X, Yuan S (2022) CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy. Eur Radiol 32(3):1538–1547. https://doi.org/10.1007/s00330-021-08277-y

    Article  Google Scholar 

  100. Song J, Wang L, Ng NN, Zhao M, Shi J, Wu N, Li W, Liu Z, Yeom KW, Tian J (2020) Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant-Positive Non-Small Cell Lung Cancer. JAMA Netw Open 3(12):e2030442. https://doi.org/10.1001/jamanetworkopen.2020.30442

  101. Wang Shuo Yu, He GY, Zhangjie Wu et al (2022) Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. The Lancet Digital Health 4(5):e309–e319. https://doi.org/10.1016/s2589-7500(22)00024-3

    Article  Google Scholar 

  102. Huang L, Chen J, Hu W, Xu X, Liu D, Wen J, Lu J, Cao J, Zhang J, Gu Y, Wang J, Fan M (2019) Assessment of a Radiomic Signature Developed in a General NSCLC Cohort for Predicting Overall Survival of ALK-Positive Patients With Different Treatment Types. Clin Lung Cancer 20(6):e638–e651. https://doi.org/10.1016/j.cllc.2019.05.005

    Article  Google Scholar 

  103. Hou R, Li X, **ong J, Shen T, Yu W, Schwartz LH, Zhao B, Zhao J, Fu X (2021) Predicting Tyrosine Kinase Inhibitor Treatment Response in Stage IV Lung Adenocarcinoma Patients With EGFR Mutation Using Model-Based Deep Transfer Learning. Front Oncol 11:679764. https://doi.org/10.3389/fonc.2021.679764

  104. Shao D, Du D, Liu H, Lv J, Cheng Y, Zhang H, Lv W, Wang S, Lu L (2021) Identification of Stage IIIC/IV EGFR-Mutated Non-Small Cell Lung Cancer Populations Sensitive to Targeted Therapy Based on a PET/CT Radiomics Risk Model. Front Oncol 11:721318. https://doi.org/10.3389/fonc.2021.721318

  105. Jazieh K, Khorrami M, Saad A, Gad M, Gupta A, Patil P, Viswanathan VS, Rajiah P, Nock CJ, Gilkey M, Fu P, Pennell NA, Madabhushi A (2022) Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab. J Immunother Cancer 10(3):e003778. https://doi.org/10.1136/jitc-2021-003778

  106. Liu Y, Wu M, Zhang Y, Luo Y, He S, Wang Y, Chen F, Liu Y, Yang Q, Li Y, Wei H, Zhang H, ** C, Lu N, Li W, Wang S, Guo Y, Ye Z (2021) Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer. Front Oncol 11:657615. https://doi.org/10.3389/fonc.2021.657615

  107. He B, Dong D, She Y, Zhou C, Fang M, Zhu Y, Zhang H, Huang Z, Jiang T, Tian J, Chen C (2020) Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. J Immunother Cancer 8(2):e000550. https://doi.org/10.1136/jitc-2020-000550

  108. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749–762. https://doi.org/10.1038/nrclinonc.2017.141

    Article  Google Scholar 

  109. Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL et al (2020) The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenoty**. Radiology 295(2):328–338. https://doi.org/10.1148/radiol.2020191145

    Article  Google Scholar 

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  1. Department of ECE, G M Institute of Technology, Affiliated to Visvesvaraya Technological University, Belagavi—590018, Davangere, Karnataka, India

    Raviteja Balekai

  2. Department of E&IE, University B.D.T College of Engineering, A Constituent College of Visvesvaraya Technological University, Belagavi—590018, Davangere, Karnataka, India

    Mallikarjun S. Holi

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Balekai, R., Holi, M.S. Exploring the potential of Radiomics in identification and treatment of lung cancer: A systematic evaluation. Multimed Tools Appl 83, 60469–60492 (2024). https://doi.org/10.1007/s11042-023-17922-1

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