Artificial Intelligence Assisted F-FDG PET Radiomics in Classifying Histological Subtypes of Lung Cancer: Systematic Review and Meta-analysis

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

Article  PubMed  Google Scholar 

Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70:7–30.

Article  PubMed  Google Scholar 

Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature. 2018;553:446–54.

Article  CAS  PubMed  Google Scholar 

Zappa C, Mousa SA. Non-small cell lung cancer: current treatment and future advances. Transl Lung Cancer Res. 2016;5:288–300.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Knight SB, Crosbie PA, Balata H, Chudziak J, Hussell T, Dive C. Progress and prospects of early detection in lung cancer. Open Biol. 2017;7:170070. https://doi.org/10.1098/rsob.170070.

Article  CAS  Google Scholar 

Thomas A, Liu SV, Subramaniam DS, Giaccone G. Refining the treatment of NSCLC according to histological and molecular subtypes. Nat Rev Clin Oncol. 2015;12:511–26.

Article  CAS  PubMed  Google Scholar 

Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, et al. The 2015 world health organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol. 2015;10:1243–60.

Article  PubMed  Google Scholar 

Ebrahimi M, Auger M, Jung S, Fraser RS. Diagnostic concordance of non–small cell lung carcinoma subtypes between biopsy and cytology specimens obtained during the same procedure. Cancer Cytopathol. 2016;124:737–43.

Article  PubMed  Google Scholar 

Gal AA. Use and abuse of lung biopsy. Adv Anat Pathol. 2005;12:195–202.

Article  PubMed  Google Scholar 

Biancosino C, Krüger M, Vollmer E, Welker L. Intraoperative fine needle aspirations - diagnosis and typing of lung cancer in small biopsies: challenges and limitations. Diagn Pathol. 2016;11:59.

Article  PubMed  PubMed Central  Google Scholar 

Koh YW, Lee SJ, Park SY. Differential expression and prognostic significance of GLUT1 according to histologic type of non-small-cell lung cancer and its association with volume-dependent parameters. Lung Cancer. 2017;104:31–7.

Article  PubMed  Google Scholar 

de Margerie-Mellon C, de Bazelaire C, de Kerviler E. Image-guided biopsy in primary lung cancer: why, when and how. Diagn Interv Imaging. 2016;97:965–72.

Article  PubMed  Google Scholar 

Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–62.

Article  PubMed  Google Scholar 

Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278:563–77.

Article  PubMed  Google Scholar 

Jha AK, Mithun S, Sherkhane UB, Dwivedi P, Puts S, Osong B, et al. Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology. Explor Target Antitumor Ther. 2023;4:569–82.

Article  PubMed  PubMed Central  Google Scholar 

Aerts HJWL, Velazquez ER, Leijenaar RTH, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.

Article  CAS  PubMed  Google Scholar 

Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJR. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40:133–40.

Article  PubMed  Google Scholar 

Ma Y, Feng W, Wu Z, Liu M, Zhang F, Liang Z, et al. Intra-tumoural heterogeneity characterization through texture and colour analysis for differentiation of non-small cell lung carcinoma subtypes. Phys Med Biol. 2018;63:165018.

Article  PubMed  Google Scholar 

Wong CW, Chaudhry A. Radiogenomics of lung cancer. J Thorac Dis. 2020;12:5104–9.

Article  PubMed  PubMed Central  Google Scholar 

Lee G, Park H, Bak SH, Lee HY. Radiomics in lung cancer from basic to advanced: current status and future directions. Korean J Radiol. 2020;21:159.

Article  PubMed  PubMed Central  Google Scholar 

Wu Y-J, Wu F-Z, Yang S-C, Tang E-K, Liang C-H. Radiomics in early lung cancer diagnosis: from diagnosis to clinical decision support and education. Diagnostics. 2022;12:1064.

Article  PubMed  PubMed Central  Google Scholar 

Kirienko M, Cozzi L, Rossi A, Voulaz E, Antunovic L, Fogliata A, et al. Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging. 2018;45:1649–60.

Article  PubMed  Google Scholar 

Sha X, Gong G, Qiu Q, Duan J, Li D, Yin Y. Identifying pathological subtypes of non-small-cell lung cancer by using the radiomic features of 18F-fluorodeoxyglucose positron emission computed tomography. Transl Cancer Res. 2019;8:1741–9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Koyasu S, Nishio M, Isoda H, Nakamoto Y, Togashi K. Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT. Ann Nucl Med. 2020;34:49–57.

Article  CAS  PubMed  Google Scholar 

Hyun SH, Ahn MS, Koh YW, Lee SJ. A Machine-learning approach using PET-based radiomics to predict the histological subtypes of lung cancer. Clin Nucl Med. 2019;44:956–60.

Article  PubMed  Google Scholar 

Han Y, Ma Y, Wu Z, Zhang F, Zheng D, Liu X, et al. Histologic subtype classification of non-small cell lung cancer using PET/CT images. Eur J Nucl Med Mol Imaging. 2021;48:350–60.

Article  PubMed  Google Scholar 

Ji Y, Qiu Q, Fu J, Cui K, Chen X, Xing L, et al. Stage-specific PET radiomic prediction model for the histological subtype classification of non-small-cell lung cancer. Cancer Manag Res. 2021;13:307–17.

Article  PubMed  PubMed Central  Google Scholar 

Zhou Y, Ma X, lei, Zhang T, Wang J, Zhang T, Tian R. Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach. Eur J Nucl Med Mol Imaging. 2021;48:2904–13.

Article  PubMed  Google Scholar 

Tang X, Wu J, Liang J, Yuan C, Shi F, Ding Z. The value of combined PET/MRI, CT and clinical metabolic parameters in differentiating lung adenocarcinoma from squamous cell carcinoma. Front Oncol. 2022;12:991102.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Dondi F, Gatta R, Albano D, Bellini P, Camoni L, Treglia G, et al. Role of radiomics features and machine learning for the histological classification of stage I and stage II NSCLC at [18F]FDG PET/CT: a comparison between two PET/CT scanners. J Clin Med. 2023;12:255.

Article  CAS  Google Scholar 

Zhang Y, Liu H, Chang C, Yin Y, Wang R. Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using clinical-metabolic characteristics and 18F-FDG PET/CT radiomics. PLoS ONE. 2024;19:e0300170.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Khodabakhshi Z, Amini M, Hajianfar G, Oveisi M, Shiri I, Zaidi H. Dual-centre harmonised multimodal positron emission tomography/computed tomography image radiomic features and machine learning algorithms for non-small cell lung cancer histopathological subtype phenotype decoding. Clin Oncol. 2023;35:713–25.

Article  CAS  Google Scholar 

Ren C, Zhang J, Qi M, Zhang J, Zhang Y, Song S, et al. Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung. Eur J Nucl Med Mol Imagin

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