Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

References

• Coche E, Lonneux M, Geets X. Lung cancer: morphological and functional approach to screening, staging and treatment planning. Future Oncol. 2010;6(3):367–380. doi:10.2217/fon.10.7
   PubMed Web of Science ®Google Scholar
• Duma N, Santana-Davila R, Molina JR. Non-small cell lung cancer: epidemiology, screening, diagnosis, and treatment. Mayo Clin Proc. 2019;94(8):1623–1640. doi:10.1016/j.mayocp.2019.01.013
   PubMed Web of Science ®Google Scholar
• Jones GS, Baldwin DR. Recent advances in the management of lung cancer. Clin Med. 2018;18(Suppl 2):s41–s46. doi:10.7861/clinmedicine.18-2-s41
   PubMed Web of Science ®Google Scholar
• de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. New Engl J Med. 2020;382(6):503–513. doi:10.1056/NEJMoa1911793
   PubMed Web of Science ®Google Scholar
• Goldstraw P, Chansky K, Crowley J, et al. The IASLC lung cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (Eighth) Edition of the TNM classification for lung cancer. J Thorac Oncol. 2016;11(1):39–51. doi:10.1016/j.jtho.2015.09.009
   PubMed Web of Science ®Google Scholar
• Arbour KC, Riely GJ. Systemic therapy for locally advanced and metastatic non-small cell lung cancer: a review. JAMA. 2019;322(8):764–774. doi:10.1001/jama.2019.11058
   PubMed Web of Science ®Google Scholar
• Kapoor V, McCook BM, Torok FS. An introduction to PET-CT imaging. Radiographics. 2004;24(2):523–543. doi:10.1148/rg.242025724
   PubMed Web of Science ®Google Scholar
• Hoffmann B, Frenzel T, Schmitz R, Schumacher U, Wedemann G. Modeling Growth of tumors and their spreading behavior using mathematical functions. Methods Mol Biol. 2019;1878:263–277.
   PubMedGoogle Scholar
• Wisnivesky JP, Henschke C, McGinn T, Iannuzzi MC. Prognosis of Stage II non-small cell lung cancer according to tumor and nodal status at diagnosis. Lung Cancer. 2005;49(2):181–186. doi:10.1016/j.lungcan.2005.02.010
   PubMed Web of Science ®Google Scholar
• Szyszko TA, Yip C, Szlosarek P, Goh V, Cook GJ. The role of new PET tracers for lung cancer. Lung Cancer. 2016;94:7–14. doi:10.1016/j.lungcan.2016.01.010
   PubMed Web of Science ®Google Scholar
• Iravani A, Turgeon GA, Akhurst T, et al. PET-detected pneumonitis following curative-intent chemoradiation in non-small cell lung cancer (NSCLC): recognizing patterns and assessing the impact on the predictive ability of FDG-PET/CT response assessment. Eur J Nucl Med Mol Imaging. 2019;46(9):1869–1877. doi:10.1007/s00259-019-04388-3
   PubMed Web of Science ®Google Scholar
• Amisha Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Fam Med Prim Care. 2019;8(7):2328–2331. doi:10.4103/jfmpc.jfmpc_440_19
   PubMedGoogle Scholar
• Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minimally Invasive Ther Allied Technol. 2019;28(2):73–81. doi:10.1080/13645706.2019.1575882
   PubMed Web of Science ®Google Scholar
• Milne-Ives M, de Cock C, Lim E, et al. The effectiveness of artificial intelligence conversational agents in health care: systematic review. J Med Int Res. 2020;22(10):e20346. doi:10.2196/20346
   Google Scholar
• Han Z, Wei B, Xi X, Chen B, Yin Y, Li S. Unifying neural learning and symbolic reasoning for spinal medical report generation. Med Image Anal. 2021;67:101872. doi:10.1016/j.media.2020.101872
   PubMed Web of Science ®Google Scholar
• Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395(10236):1579–1586. doi:10.1016/S0140-6736(20)30226-9
   PubMed Web of Science ®Google Scholar
• van Zijl A, van Loon M, ten Cate O. Case-Based clinical reasoning in practice. In: ten Cate O, Custers E, Durning SJ, editors. Principles and Practice of Case-Based Clinical Reasoning Education: A Method for Preclinical Students. Cham (CH): Springer Copyright 2018, The Author(s); 2018:75–83.
   Google Scholar
• Vlamou E, Papadopoulos B. Neuro-fuzzy networks and their applications in medical fields. Adv Exp Med Biol. 2020;1194:437.
   PubMed Web of Science ®Google Scholar
• Ao C, Jin S, Ding H, Zou Q, Yu L. Application and development of artificial intelligence and intelligent disease diagnosis. Curr Pharm Des. 2020;26(26):3069–3075. doi:10.2174/1381612826666200331091156
   PubMed Web of Science ®Google Scholar
• Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022;23(1):40–55. doi:10.1038/s41580-021-00407-0
   PubMed Web of Science ®Google Scholar
• Martín Noguerol T, Paulano-Godino F, Martín-Valdivia MT, Menias CO, Luna A. Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology. J Am College Radiol. 2019;16(9 Pt B):1239–1247. doi:10.1016/j.jacr.2019.05.047
   PubMed Web of Science ®Google Scholar
• Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Internal Med. 2018;284(6):603–619. doi:10.1111/joim.12822
   Google Scholar
• Aslin RN. Statistical learning: a powerful mechanism that operates by mere exposure. Wiley interdisciplinary reviews. Cognit Sci. 2017;8(1–2). doi:10.1002/wcs.1373
   Web of Science ®Google Scholar
• Jiang T, Gradus JL, Rosellini AJ. Supervised Machine learning: a brief primer. Behav Ther. 2020;51(5):675–687. doi:10.1016/j.beth.2020.05.002
   PubMed Web of Science ®Google Scholar
• Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Trans Vision Sci Technol. 2020;9(2):14.
   PubMed Web of Science ®Google Scholar
• Rodríguez-Pérez R, Bajorath J. Evolution of support vector machine and regression modeling in chemoinformatics and drug discovery. J Comput Aided Molec Design. 2022;36(5):355–362. doi:10.1007/s10822-022-00442-9
   PubMed Web of Science ®Google Scholar
• Hu J, Szymczak S. A review on longitudinal data analysis with random forest. Briefings Bioinf. 2023;24(2). doi:10.1093/bib/bbad002
   Web of Science ®Google Scholar
• Karalis G. Decision Trees and Applications. Adv Exp Med Biol. 2020;1194:239–242.
   PubMed Web of Science ®Google Scholar
• Schober P, Vetter TR. Logistic regression in medical research. Anesthesia Analg. 2021;132(2):365–366. doi:10.1213/ANE.0000000000005247
   PubMed Web of Science ®Google Scholar
• Zhang Z. Introduction to machine learning: k-nearest neighbors. Ann Translat Med. 2016;4(11):218. doi:10.21037/atm.2016.03.37
   PubMed Web of Science ®Google Scholar
• Heng SY, Ridwan WM, Kumar P, et al. Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction. Sci Rep. 2022;12(1):10457. doi:10.1038/s41598-022-13532-3
   PubMed Web of Science ®Google Scholar
• Zheng Z, Yang Y. Adaptive boosting for domain adaptation: toward robust predictions in scene segmentation. IEEE Trans Image Process. 2022;31:5371–5382. doi:10.1109/TIP.2022.3195642
   PubMed Web of Science ®Google Scholar
• Jiang Y, Yang M, Wang S, Li X, Sun Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun. 2020;40(4):154–166. doi:10.1002/cac2.12012
   Web of Science ®Google Scholar
• Araújo T, Aresta G, Castro E, et al. Classification of breast cancer histology images using convolutional neural networks. PLoS One. 2017;12(6):e0177544. doi:10.1371/journal.pone.0177544
   PubMed Web of Science ®Google Scholar
• Derry A, Krzywinski M, Altman N. Convolutional neural networks. Nature Methods. 2023;20(9):1269–1270. doi:10.1038/s41592-023-01973-1
   PubMed Web of Science ®Google Scholar
• Wang L, Ding L, Liu Z, et al. Automated identification of malignancy in whole-slide pathological images: identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning. Br J Ophthalmol. 2020;104(3):318–323. doi:10.1136/bjophthalmol-2018-313706
   PubMed Web of Science ®Google Scholar
• Ehteshami Bejnordi B, Mullooly M, Pfeiffer RM, et al. Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies. Mod Pathol. 2018;31(10):1502–1512. doi:10.1038/s41379-018-0073-z
   PubMed Web of Science ®Google Scholar
• Ciompi F, Chung K, van Riel SJ, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci Rep. 2017;7(1):46479. doi:10.1038/srep46479
   PubMedGoogle Scholar
• Liu Z, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics. 2019;9(5):1303–1322. doi:10.7150/thno.30309
   PubMed Web of Science ®Google Scholar
• Avanzo M, Wei L, Stancanello J, et al. Machine and deep learning methods for radiomics. Med Phys. 2020;47(5):e185–e202. doi:10.1002/mp.13678
   PubMed Web of Science ®Google Scholar
• Deist TM, Dankers F, Valdes G, et al. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: an empirical comparison of classifiers. Med Phys. 2018;45(7):3449–3459. doi:10.1002/mp.12967
   PubMed Web of Science ®Google Scholar
• Shi L, He Y, Yuan Z, et al. Radiomics for response and outcome assessment for non-small cell lung cancer. Technol Cancer Res Treat. 2018;17:1533033818782788. doi:10.1177/1533033818782788
   Web of Science ®Google Scholar
• Nensa F, Demircioglu A, Rischpler C. Artificial intelligence in nuclear medicine. J Nucl Med. 2019;60(Suppl 2):29s–37s. doi:10.2967/jnumed.118.220590
   PubMedGoogle Scholar
• Seifert R, Weber M, Kocakavuk E, Rischpler C, Kersting D. Artificial intelligence and machine learning in nuclear medicine: future perspectives. Semi Nucl Med. 2021;51(2):170–177. doi:10.1053/j.semnuclmed.2020.08.003
   PubMed Web of Science ®Google Scholar
• Lee LIT, Kanthasamy S, Ayyalaraju RS, Ganatra R. The current state of artificial intelligence in medical imaging and nuclear medicine. BJR Open. 2019;1(1):20190037. doi:10.1259/bjro.20190037
   PubMedGoogle Scholar
• Ninatti G, Kirienko M, Neri E, Sollini M, Chiti A. Imaging-based prediction of molecular therapy targets in NSCLC by radiogenomics and AI approaches: a systematic review. Diagnostics. 2020;10(6):359. doi:10.3390/diagnostics10060359
   Web of Science ®Google Scholar
• Lv Z, Fan J, Xu J, et al. Value of (18)F-FDG PET/CT for predicting EGFR mutations and positive ALK expression in patients with non-small cell lung cancer: a retrospective analysis of 849 Chinese patients. Eur J Nucl Med Mol Imaging. 2018;45(5):735–750. doi:10.1007/s00259-017-3885-z
   PubMed Web of Science ®Google Scholar
• Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med. 2021;13(1):152. doi:10.1186/s13073-021-00968-x
   PubMed Web of Science ®Google Scholar
• Sadaghiani MS, Rowe SP, Sheikhbahaei S. Applications of artificial intelligence in oncologic (18)F-FDG PET/CT imaging: a systematic review. Ann Translat Med. 2021;9(9):823. doi:10.21037/atm-20-6162
   PubMed Web of Science ®Google Scholar
• Swanson K, Wu E, Zhang A, Alizadeh AA, Zou J. From patterns to patients: advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. Cell. 2023;186(8):1772–1791. doi:10.1016/j.cell.2023.01.035
   PubMed Web of Science ®Google Scholar
• Wood DE, Kazerooni EA, Baum SL, et al. Lung Cancer screening, version 3.2018, NCCN clinical practice guidelines in oncology. J National Compr Cancer Network. 2018;16(4):412–441. doi:10.6004/jnccn.2018.0020
   PubMed Web of Science ®Google Scholar
• Fletcher JW, Kymes SM, Gould M, et al. A comparison of the diagnostic accuracy of 18F-FDG PET and CT in the characterization of solitary pulmonary nodules. J Nucl Med. 2008;49(2):179–185. doi:10.2967/jnumed.107.044990
   PubMed Web of Science ®Google Scholar
• Ruilong Z, Daohai X, Li G, Xiaohong W, Chunjie W, Lei T. Diagnostic value of 18F-FDG-PET/CT for the evaluation of solitary pulmonary nodules: a systematic review and meta-analysis. Nuclear med commun. 2017;38(1):67–75. doi:10.1097/MNM.0000000000000605
   PubMed Web of Science ®Google Scholar
• Li ZZ, Huang YL, Song HJ, Wang YJ, Huang Y. The value of 18F-FDG-PET/CT in the diagnosis of solitary pulmonary nodules: a meta-analysis. Medicine. 2018;97:12.
   Web of Science ®Google Scholar
• Sim Y, Chung MJ, Kotter E, et al. Deep convolutional neural network-based software improves radiologist detection of malignant lung nodules on chest radiographs. Radiology. 2020;294(1):199–209. doi:10.1148/radiol.2019182465
   PubMed Web of Science ®Google Scholar
• Kirienko M, Gallivanone F, Sollini M, et al. FDG PET/CT as theranostic imaging in diagnosis of non-small cell lung cancer. Front Biosci. 2017;22(10):1713–1723. doi:10.2741/4567
   Google Scholar
• Soufi M, Kamali-Asl A, Geramifar P, Rahmim A. A novel framework for automated segmentation and labeling of homogeneous versus heterogeneous lung tumors in [(18)F]FDG-PET imaging. Mole Imag Biol. 2017;19(3):456–468. doi:10.1007/s11307-016-1015-0
   PubMed Web of Science ®Google Scholar
• Bug D, Feuerhake F, Oswald E, Schüler J, Merhof D. Semi-automated analysis of digital whole slides from humanized lung-cancer xenograft models for checkpoint inhibitor response prediction. Oncotarget. 2019;10(44):4587–4597. doi:10.18632/oncotarget.27069
   PubMedGoogle Scholar
• Borrelli P, Ly J, Kaboteh R, et al. AI-based detection of lung lesions in [(18)F]FDG PET-CT from lung cancer patients. EJNMMI Phys. 2021;8(1):32. doi:10.1186/s40658-021-00376-5
   PubMed Web of Science ®Google Scholar
• Wang K, Qiao Z, Zhao X, et al. Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model. Eur J Nucl Med Mol Imaging. 2020;47(6):1400–1411. doi:10.1007/s00259-019-04604-0
   PubMed Web of Science ®Google Scholar
• Zhang R, Zhu L, Cai Z, et al. Potential feature exploration and model development based on 18F-FDG PET/CT images for differentiating benign and malignant lung lesions. Eur J Radiol. 2019;121:108735. doi:10.1016/j.ejrad.2019.108735
   PubMed Web of Science ®Google Scholar
• Zhao J, Ji G, Qiang Y, Han X, Pei B, Shi Z. A new method of detecting pulmonary nodules with PET/CT based on an improved watershed algorithm. PLoS One. 2015;10:4.
   Web of Science ®Google Scholar
• Chen S, Harmon S, Perk T, et al. Diagnostic classification of solitary pulmonary nodules using dual time (18)F-FDG PET/CT image texture features in granuloma-endemic regions. Sci Rep. 2017;7(1):9370. doi:10.1038/s41598-017-08764-7
   PubMedGoogle Scholar
• Visvikis D, Hatt M, Tixier F, Cheze Le Rest C. The age of reason for FDG PET image-derived indices. Eur J Nucl Med Mol Imaging. 2012;39(11):1670–1672. doi:10.1007/s00259-012-2239-0
   PubMed Web of Science ®Google Scholar
• Alkhawaldeh K, Bural G, Kumar R, Alavi A. Impact of dual-time-point (18)F-FDG PET imaging and partial volume correction in the assessment of solitary pulmonary nodules. Eur J Nucl Med Mol Imaging. 2008;35(2):246–252. doi:10.1007/s00259-007-0584-1
   PubMed Web of Science ®Google Scholar
• Zhang J, Ma G, Cheng J, Song S, Zhang Y, Shi LQ. Diagnostic classification of solitary pulmonary nodules using support vector machine model based on 2-[18F]fluoro-2-deoxy-D-glucose PET/computed tomography texture features. Nuclear Med Commun. 2020;41(6):560–566. doi:10.1097/MNM.0000000000001193
   PubMed Web of Science ®Google Scholar
• Park YJ, Choi D, Choi JY, Hyun SH. Performance evaluation of a deep learning system for differential diagnosis of lung cancer with conventional CT and FDG PET/CT using transfer learning and metadata. Clin Nucl Med. 2021;46(8):635–640. doi:10.1097/RLU.0000000000003661
   PubMed Web of Science ®Google Scholar
• Teramoto A, Fujita H, Yamamuro O, Tamaki T. Automated detection of pulmonary nodules in PET/CT images: ensemble false-positive reduction using a convolutional neural network technique. Med Phys. 2016;43(6):2821–2827. doi:10.1118/1.4948498
   PubMed Web of Science ®Google Scholar
• Scott JA, McDermott S, Kilcoyne A, Wang Y, Halpern EF, Ackman JB. Comparison of (18)F-FDG avidity at PET of benign and malignant pure ground-glass opacities: a paradox? Part II: artificial neural network integration of the PET/CT characteristics of ground-glass opacities to predict their likelihood of malignancy. Clin Radiol. 2019;74(9):692–696. doi:10.1016/j.crad.2019.04.024
   PubMed Web of Science ®Google Scholar
• Schwyzer M, Ferraro DA, Muehlematter UJ, et al. Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks - Initial results. Lung Cancer. 2018;126:170–173. doi:10.1016/j.lungcan.2018.11.001
   PubMed Web of Science ®Google Scholar
• Leung KH, Marashdeh W, Wray R, et al. A physics-guided modular deep-learning based automated framework for tumor segmentation in PET. Phys Med Biol. 2020;65(24):245032. doi:10.1088/1361-6560/ab8535
   PubMed Web of Science ®Google Scholar
• Ikushima K, Arimura H, Jin Z, et al. Computer-assisted framework for machine-learning-based delineation of GTV regions on datasets of planning CT and PET/CT images. J Radiat Res. 2017;58(1):123–134. doi:10.1093/jrr/rrw082
   PubMed Web of Science ®Google Scholar
• Protonotarios NE, Katsamenis I, Sykiotis S, et al. A few-shot U-Net deep learning model for lung cancer lesion segmentation via PET/CT imaging. Biomed Phys Eng Express. 2022;8(2):025019. doi:10.1088/2057-1976/ac53bd
   Web of Science ®Google Scholar
• Gubbi J, Kanakatte A, Tomas K, et al. Automatic tumour volume delineation in respiratory-gated PET images. J Med Imag Radiat Oncol. 2011;55(1):65–76. doi:10.1111/j.1754-9485.2010.02231.x
   PubMed Web of Science ®Google Scholar
• Zhong Z, Kim Y, Plichta K, et al. Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks. Med Phys. 2019;46(2):619–633. doi:10.1002/mp.13331
   PubMed Web of Science ®Google Scholar
• Kawata Y, Arimura H, Ikushima K, et al. Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy. Phys Med. 2017;42:141–149. doi:10.1016/j.ejmp.2017.08.012
   PubMed Web of Science ®Google Scholar
• Scagliotti G, Hanna N, Fossella F, et al. The differential efficacy of pemetrexed according to NSCLC histology: a review of two Phase III studies. Oncologist. 2009;14(3):253–263. doi:10.1634/theoncologist.2008-0232
   PubMed Web of Science ®Google Scholar
• Khosravi P, Kazemi E, Imielinski M, Elemento O, Hajirasouliha I. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine. 2018;27:317–328. doi:10.1016/j.ebiom.2017.12.026
   PubMed Web of Science ®Google Scholar
• Feng P, Shao Z, Dong B, et al. Application of diffusion kurtosis imaging and (18)F-FDG PET in evaluating the subtype, stage and proliferation status of non-small cell lung cancer. Front Oncol. 2022;12:989131. doi:10.3389/fonc.2022.989131
   PubMed Web of Science ®Google Scholar
• Ha S, Choi H, Cheon GJ, et al. Autoclustering of non-small cell lung carcinoma subtypes on (18)F-FDG PET using texture analysis: a preliminary result. Nucl Med molec Imag. 2014;48(4):278–286. doi:10.1007/s13139-014-0283-3
   PubMedGoogle 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 (18)F FDG-PET/CT. Ann Nucl Med. 2020;34(1):49–57. doi:10.1007/s12149-019-01414-0
   PubMed Web of Science ®Google Scholar
• Ren C, Zhang J, Qi M, et al. Correction to: 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 Imaging. 2021;48(5):1696. doi:10.1007/s00259-021-05226-1
   PubMed Web of Science ®Google Scholar
• Han Y, Ma Y, Wu Z, et al. Histologic subtype classification of non-small cell lung cancer using PET/CT images. Eur J Nucl Med Mol Imaging. 2021;48(2):350–360. doi:10.1007/s00259-020-04771-5
   PubMed Web of Science ®Google Scholar
• Dondi F, Gatta R, Albano D, et al. Role of radiomics features and machine learning for the histological classification of stage I and stage II NSCLC at [(18)F]FDG PET/CT: a comparison between two PET/CT scanners. J Clin Med. 2022;12(1). doi:10.3390/jcm12010255
   PubMed Web of Science ®Google Scholar
• Shen H, Chen L, Liu K, et al. A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes. Quantit Imag Med Surg. 2021;11(7):2918–2932. doi:10.21037/qims-20-1182
   PubMed Web of Science ®Google Scholar
• Yang G, Xiao Z, Tang C, Deng Y, Huang H, He Z. Recent advances in biosensor for detection of lung cancer biomarkers. Biosens Bioelectron. 2019;141:111416. doi:10.1016/j.bios.2019.111416
   PubMed Web of Science ®Google Scholar
• Zhao H, Su Y, Wang M, et al. The machine learning model for distinguishing pathological subtypes of non-small cell lung cancer. Front Oncol. 2022;12:875761. doi:10.3389/fonc.2022.875761
   PubMed Web of Science ®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;104:31–37. doi:10.1016/j.lungcan.2016.12.003
   PubMed Web of Science ®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(12):956–960. doi:10.1097/RLU.0000000000002810
   PubMed Web of Science ®Google Scholar
• Zhao M, Kluge K, Papp L, et al. Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma. Eur Radiol. 2022;32(10):7056–7067. doi:10.1007/s00330-022-08999-7
   PubMed Web of Science ®Google Scholar
• Kris MG, Johnson BE, Berry LD, et al. Using multiplexed assays of oncogenic drivers in lung cancers to select targeted drugs. JAMA. 2014;311(19):1998–2006. doi:10.1001/jama.2014.3741
   PubMed Web of Science ®Google Scholar
• Midha A, Dearden S, McCormack R. EGFR mutation incidence in non-small-cell lung cancer of adenocarcinoma histology: a systematic review and global map by ethnicity (mutMapII). Am J Canc Res. 2015;5(9):2892–2911.
   PubMed Web of Science ®Google Scholar
• Park K, Tan EH, O’Byrne K, et al. Afatinib versus gefitinib as first-line treatment of patients with EGFR mutation-positive non-small-cell lung cancer (LUX-Lung 7): a phase 2B, open-label, randomised controlled trial. Lancet Oncol. 2016;17(5):577–589. doi:10.1016/S1470-2045(16)30033-X
   PubMed Web of Science ®Google Scholar
• Nair JKR, Saeed UA, McDougall CC, et al. Radiogenomic models using machine learning techniques to predict EGFR mutations in non-small cell lung cancer. Can Assoc Radiol J. 2021;72(1):109–119. doi:10.1177/0846537119899526
   PubMed Web of Science ®Google Scholar
• Sha L, Osinski BL, Ho IY, et al. Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images. J Pathol Inform. 2019;10(1):24. doi:10.4103/jpi.jpi_24_19
   PubMedGoogle Scholar
• Truini A, Starrett JH, Stewart T, et al. The EGFR exon 19 mutant L747-A750>P exhibits distinct sensitivity to tyrosine kinase inhibitors in lung adenocarcinoma. Clin Cancer Res. 2019;25(21):6382–6391. doi:10.1158/1078-0432.CCR-19-0780
   PubMed Web of Science ®Google Scholar
• An N, Zhang Y, Niu H, et al. EGFR-TKIs versus taxanes agents in therapy for nonsmall-cell lung cancer patients: a PRISMA-compliant systematic review with meta-analysis and meta-regression. Medicine. 2016;95(50):e5601. doi:10.1097/MD.0000000000005601
   PubMed Web of Science ®Google Scholar
• Giatromanolaki A, Koukourakis IM, Balaska K, Mitrakas AG, Harris AL, Koukourakis MI. Programmed death-1 receptor (PD-1) and PD-ligand-1 (PD-L1) expression in non-small cell lung cancer and the immune-suppressive effect of anaerobic glycolysis. Med Oncol. 2019;36(9):76. doi:10.1007/s12032-019-1299-4
   PubMed Web of Science ®Google Scholar
• Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nature Med. 2018;24(10):1559–1567. doi:10.1038/s41591-018-0177-5
   PubMed Web of Science ®Google Scholar
• Sun X, Xiao Z, Chen G, et al. A PET imaging approach for determining EGFR mutation status for improved lung cancer patient management. Sci Trans Med. 2018;10(431). doi:10.1126/scitranslmed.aan8840
   Web of Science ®Google Scholar
• Mu W, Jiang L, Zhang J, et al. Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nat Commun. 2020;11(1):5228. doi:10.1038/s41467-020-19116-x
   PubMed Web of Science ®Google Scholar
• Chen S, Han X, Tian G, et al. Using stacked deep learning models based on PET/CT images and clinical data to predict EGFR mutations in lung cancer. Front Med. 2022;9:1041034. doi:10.3389/fmed.2022.1041034
   Web of Science ®Google Scholar
• Yang B, Ji H, Zhong J, et al. Value of (18)F-FDG PET/CT-based radiomics nomogram to predict survival outcomes and guide personalized targeted therapy in lung adenocarcinoma with EGFR mutations. Front Oncol. 2020;10:567160. doi:10.3389/fonc.2020.567160
   PubMed Web of Science ®Google Scholar
• Liu Q, Sun D, Li N, et al. Predicting EGFR mutation subtypes in lung adenocarcinoma using (18)F-FDG PET/CT radiomic features. Transl Lung Canc Res. 2020;9(3):549–562. doi:10.21037/tlcr.2020.04.17
   PubMed Web of Science ®Google Scholar
• Yip SS, Kim J, Coroller TP, et al. Associations between somatic mutations and metabolic imaging phenotypes in non-small cell lung cancer. J Nucl Med. 2017;58(4):569–576. doi:10.2967/jnumed.116.181826
   PubMed Web of Science ®Google Scholar
• Krarup MMK, Nygård L, Vogelius IR, et al. Heterogeneity in tumours: validating the use of radiomic features on (18)F-FDG PET/CT scans of lung cancer patients as a prognostic tool. Radiother Oncol. 2020;144:72–78. doi:10.1016/j.radonc.2019.10.012
   PubMed Web of Science ®Google Scholar
• Rizvi NA, Hellmann MD, Snyder A, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348(6230):124–128. doi:10.1126/science.aaa1348
   PubMed Web of Science ®Google Scholar
• Reck M, Rodríguez-Abreu D, Robinson AG, et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. New Engl J Med. 2016;375(19):1823–1833. doi:10.1056/NEJMoa1606774
   PubMed Web of Science ®Google Scholar
• Jiang M, Sun D, Guo Y, et al. Assessing PD-L1 expression level by radiomic features from PET/CT in nonsmall cell lung cancer patients: an initial result. Acad Radiol. 2020;27(2):171–179. doi:10.1016/j.acra.2019.04.016
   PubMed Web of Science ®Google Scholar
• Mu W, Jiang L, Shi Y, et al. Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images. J Immunother Canc. 2021;9(6):e002118. doi:10.1136/jitc-2020-002118
   PubMed Web of Science ®Google Scholar
• Park HJ, Park N, Lee JH, et al. Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning. BMC Med Inf Decis Making. 2022;22(1):229. doi:10.1186/s12911-022-01975-7
   PubMed Web of Science ®Google Scholar
• Bach PB, Gould MK. When the average applies to no one: personalized decision making about potential benefits of lung cancer screening. Ann Internal Med. 2012;157(8):571–573. doi:10.7326/0003-4819-157-8-201210160-00524
   PubMed Web of Science ®Google Scholar
• Choi HS, Jeong BK, Jeong H, et al. Application of the new 8th TNM staging system for non-small cell lung cancer: treated with curative concurrent chemoradiotherapy. Radiat Oncol. 2017;12(1):122. doi:10.1186/s13014-017-0848-2
   PubMedGoogle Scholar
• Zhong Z, Kim Y, Buatti J, Wu X. 3D alpha matting based co-segmentation of tumors on PET-CT images. Molecu Imag Reconstr Analy Mov Body Organs Stroke Imag Treat. 2017;10555:31–42.
   PubMedGoogle Scholar
• Yoo J, Cheon M, Park YJ, et al. Machine learning-based diagnostic method of pre-therapeutic (18)F-FDG PET/CT for evaluating mediastinal lymph nodes in non-small cell lung cancer. Eur Radiol. 2021;31(6):4184–4194. doi:10.1007/s00330-020-07523-z
   PubMed Web of Science ®Google Scholar
• Mercan C, Aksoy S, Mercan E, Shapiro LG, Weaver DL, Elmore JG. Multi-instance multi-label learning for multi-class classification of whole slide breast histopathology images. IEEE Transact Med Imag. 2018;37(1):316–325. doi:10.1109/TMI.2017.2758580
   PubMed Web of Science ®Google Scholar
• Zeune LL, de Wit S, Berghuis AMS, Terstappen L, Brune C. How to agree on a CTC: evaluating the consensus in circulating tumor cell scoring. Cytometry. 2018;93(12):1202–1206. doi:10.1002/cyto.a.23576
   PubMedGoogle Scholar
• Gould MK, Huang BZ, Tammemagi MC, Kinar Y, Shiff R. Machine learning for early lung cancer identification using routine clinical and laboratory data. Am J Respir Crit Care Med. 2021;204(4):445–453. doi:10.1164/rccm.202007-2791OC
   PubMed Web of Science ®Google Scholar
• Kirienko M, Cozzi L, Rossi 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(10):1649–1660. doi:10.1007/s00259-018-3987-2
   PubMed Web of Science ®Google Scholar
• Rogasch JMM, Michaels L, Baumgärtner GL, et al. A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [18F]FDG-PET/CT parameters. Eur J Nucl Med Mol Imaging. 2023;50(7):2140–2151. doi:10.1007/s00259-023-06145-z
   PubMed Web of Science ®Google Scholar
• Kirienko M, Sollini M, Silvestri G, et al. Convolutional neural networks promising in lung cancer T-parameter assessment on baseline FDG-PET/CT. Contrast Media Mole Imag. 2018;2018:1382309. doi:10.1155/2018/1382309
   PubMedGoogle Scholar
• Tau N, Stundzia A, Yasufuku K, Hussey D, Metser U. Convolutional neural networks in predicting nodal and distant metastatic potential of newly diagnosed non-small cell lung cancer on FDG PET images. Am J Roentgenol. 2020;215(1):192–197. doi:10.2214/AJR.19.22346
   PubMed Web of Science ®Google Scholar
• Desseroit MC, Visvikis D, Tixier F, et al. Erratum to: development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I-III. Eur J Nucl Med Mol Imaging. 2016;43(10):1933. doi:10.1007/s00259-016-3450-1
   PubMed Web of Science ®Google Scholar
• Sepehri S, Tankyevych O, Upadhaya T, Visvikis D, Hatt M, Cheze Le Rest C. Comparison and fusion of machine learning algorithms for prospective validation of PET/CT radiomic features prognostic value in stage II-III non-small cell lung cancer. Diagnostics. 2021;11(4):675. doi:10.3390/diagnostics11040675
   Web of Science ®Google Scholar
• Yoo J, Lee J, Cheon M, et al. Predictive Value of (18)F-FDG PET/CT using machine learning for pathological response to neoadjuvant concurrent chemoradiotherapy in patients with stage III non-small cell lung cancer. Cancers. 2022;14(8):1987. doi:10.3390/cancers14081987
   PubMed Web of Science ®Google Scholar
• Koike T, Koike T, Yoshiya K, Tsuchida M, Toyabe S. Risk factor analysis of locoregional recurrence after sublobar resection in patients with clinical stage IA non-small cell lung cancer. J Thoracic Cardiovasc Surg. 2013;146(2):372–378. doi:10.1016/j.jtcvs.2013.02.057
   PubMed Web of Science ®Google Scholar
• Suzuki K, Asamura H, Kusumoto M, Kondo H, Tsuchiya R. ”Early” peripheral lung cancer: prognostic significance of ground glass opacity on thin-section computed tomographic scan. Ann Thorac Surg. 2002;74(5):1635–1639. doi:10.1016/S0003-4975(02)03895-X
   PubMed Web of Science ®Google Scholar
• Aokage K, Miyoshi T, Ishii G, et al. Clinical and pathological staging validation in the eighth edition of the TNM classification for lung cancer: correlation between solid size on thin-section computed tomography and invasive size in pathological findings in the new T classification. J Thorac Oncol. 2017;12(9):1403–1412. doi:10.1016/j.jtho.2017.06.003
   PubMed Web of Science ®Google Scholar
• Du Z, Zhong X, Wang F, Uversky VN. Inference of gene regulatory networks based on the light gradient boosting machine. Comput Biol Chem. 2022;101:107769. doi:10.1016/j.compbiolchem.2022.107769
   PubMed Web of Science ®Google Scholar
• Zheng X, He B, Hu Y, et al. Diagnostic accuracy of deep learning and radiomics in lung cancer staging: a systematic review and meta-analysis. Front Public Health. 2022;10:938113. doi:10.3389/fpubh.2022.938113
   PubMed Web of Science ®Google Scholar
• Onozato Y, Iwata T, Uematsu Y, et al. Predicting pathological highly invasive lung cancer from preoperative [(18)F]FDG PET/CT with multiple machine learning models. Eur J Nucl Med Mol Imaging. 2023;50(3):715–726. doi:10.1007/s00259-022-06038-7
   PubMed Web of Science ®Google Scholar
• Li X, Wang D, Yu L. Prognostic and Predictive values of metabolic parameters of (18)F-FDG PET/CT in patients with non-small cell lung cancer treated with chemotherapy. Molec Imag. 2019;18:1536012119846025. doi:10.1177/1536012119846025
   Web of Science ®Google Scholar
• Zhao X, Li L, Lu W, Tan S. Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network. Phys Med Biol. 2018;64(1):015011. doi:10.1088/1361-6560/aaf44b
   PubMed Web of Science ®Google Scholar
• Lustberg T, van Soest J, Gooding M, et al. Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol. 2018;126(2):312–317. doi:10.1016/j.radonc.2017.11.012
   PubMed Web of Science ®Google Scholar
• Zhou X. Automatic Segmentation of multiple organs on 3D CT images by using deep learning approaches. Adv Exp Med Biol. 2020;1213:135–147.
   PubMed Web of Science ®Google Scholar
• Zhong Z, Kim Y, Zhou L, et al. 3D Fully convolutional networks for co-segmentation of tumors on pet-Ct images. IEEE Int Sympos Biomed Imag. 2018;2018:228–231.
   PubMedGoogle Scholar
• Mattonen SA, Palma DA, Haasbeek CJ, Senan S, Ward AD. Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer. Med Phys. 2014;41(3):033502. doi:10.1118/1.4866219
   PubMed Web of Science ®Google Scholar
• Li S, Yang N, Li B, et al. A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT. Med Image Anal. 2018;50:106–116. doi:10.1016/j.media.2018.09.004
   PubMed Web of Science ®Google Scholar
• Oikonomou A, Khalvati F, Tyrrell PN, et al. Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy. Sci Rep. 2018;8(1):4003. doi:10.1038/s41598-018-22357-y
   PubMed Web of Science ®Google Scholar
• Mezquita L, Auclin E, Ferrara R, et al. Association of the lung immune prognostic index with immune checkpoint inhibitor outcomes in patients with advanced non-small cell lung cancer. JAMA Oncol. 2018;4(3):351–357. doi:10.1001/jamaoncol.2017.4771
   PubMed Web of Science ®Google Scholar
• Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017;541(7637):321–330. doi:10.1038/nature21349
   PubMed Web of Science ®Google Scholar
• Gajewski TF, Corrales L, Williams J, Horton B, Sivan A, Spranger S. Cancer immunotherapy targets based on understanding the T cell-inflamed versus non-T cell-inflamed tumor microenvironment. Adv Exp Med Biol. 2017;1036:19–31.
   PubMed Web of Science ®Google Scholar
• Binnewies M, Roberts EW, Kersten K, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nature Med. 2018;24(5):541–550. doi:10.1038/s41591-018-0014-x
   PubMed Web of Science ®Google Scholar
• Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat Rev Drug Discov. 2019;18(3):197–218. doi:10.1038/s41573-018-0007-y
   PubMed Web of Science ®Google Scholar
• Tong H, Sun J, Fang J, et al. A machine learning model based on PET/CT Radiomics and clinical characteristics predicts tumor immune profiles in non-small cell lung cancer: a retrospective multicohort study. Front Immunol. 2022;13:859323. doi:10.3389/fimmu.2022.859323
   PubMed Web of Science ®Google Scholar
• Uramoto H, Tanaka F. Prediction of recurrence after complete resection in patients with NSCLC. Anticancer Res. 2012;32(9):3953–3960.
   PubMed Web of Science ®Google Scholar
• Huh JW, Kim CH, Lim SW, Kim HR, Kim YJ. Early recurrence in patients undergoing curative surgery for colorectal cancer: is it a predictor for poor overall survival? Int J Colorec Dis. 2013;28(8):1143–1149. doi:10.1007/s00384-013-1675-z
   PubMed Web of Science ®Google Scholar
• Lou F, Sima CS, Rusch VW, Jones DR, Huang J. Differences in patterns of recurrence in early-stage versus locally advanced non-small cell lung cancer. Ann Thorac Surg. 2014;98(5):1755–1760; discussion 1760–1751. doi:10.1016/j.athoracsur.2014.05.070
   Web of Science ®Google Scholar
• Kay FU, Kandathil A, Batra K, Saboo SS, Abbara S, Rajiah P. Revisions to the tumor, node, metastasis staging of lung cancer (8(th) edition): rationale, radiologic findings and clinical implications. World J Radiol. 2017;9(6):269–279. doi:10.4329/wjr.v9.i6.269
   PubMed Web of Science ®Google Scholar
• De Wever W, Ceyssens S, Mortelmans L, et al. Additional value of PET-CT in the staging of lung cancer: comparison with CT alone, PET alone and visual correlation of PET and CT. Eur Radiol. 2007;17(1):23–32. doi:10.1007/s00330-006-0284-4
   PubMed Web of Science ®Google Scholar
• Vu CC, Matthews R, Kim B, Franceschi D, Bilfinger TV, Moore WH. Prognostic value of metabolic tumor volume and total lesion glycolysis from ¹8F-FDG PET/CT in patients undergoing stereotactic body radiation therapy for stage I non-small-cell lung cancer. Nuclear med commun. 2013;34(10):959–963. doi:10.1097/MNM.0b013e32836491a9
   PubMed Web of Science ®Google Scholar
• Liu J, Dong M, Sun X, Li W, Xing L, Yu J. Prognostic value of 18F-FDG PET/CT in surgical non-small cell lung cancer: a meta-analysis. PLoS One. 2016;11:1.
   Google Scholar
• Hosny A, Parmar C, Coroller TP, et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med. 2018;15(11):e1002711. doi:10.1371/journal.pmed.1002711
   PubMed Web of Science ®Google Scholar
• Baek S, He Y, Allen BG, et al. Deep segmentation networks predict survival of non-small cell lung cancer. Sci Rep. 2019;9(1):17286. doi:10.1038/s41598-019-53461-2
   PubMedGoogle Scholar
• Astaraki M, Wang C, Buizza G, Toma-Dasu I, Lazzeroni M, Smedby Ö. Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method. Phys Med. 2019;60:58–65. doi:10.1016/j.ejmp.2019.03.024
   PubMed Web of Science ®Google Scholar
• Ohri N, Duan F, Snyder BS, et al. Pretreatment 18F-FDG PET textural features in locally advanced non-small cell lung cancer: secondary analysis of ACRIN 6668/RTOG 0235. J Nucl Med. 2016;57(6):842–848. doi:10.2967/jnumed.115.166934
   PubMed Web of Science ®Google Scholar
• Grossmann P, Stringfield O, El-Hachem N, et al. Defining the biological basis of radiomic phenotypes in lung cancer. eLife. 2017;3:6.
   Google Scholar
• Huang B, Sollee J, Luo YH, et al. Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT. EBioMedicine. 2022;82:104127. doi:10.1016/j.ebiom.2022.104127
   PubMed Web of Science ®Google Scholar
• He J, Zhang JX, Chen CT, et al. The relative importance of clinical and socio-demographic variables in prognostic prediction in non-small cell lung cancer: a variable importance approach. Med Care. 2020;58(5):461–467. doi:10.1097/MLR.0000000000001288
   PubMed Web of Science ®Google Scholar
• Park SB, Kim KU, Park YW, Hwang JH, Lim CH. Application of 18 F-fluorodeoxyglucose PET/CT radiomic features and machine learning to predict early recurrence of non-small cell lung cancer after curative-intent therapy. Nucl Med Commun. 2023;44(2):161–168. doi:10.1097/MNM.0000000000001646
   PubMed Web of Science ®Google Scholar
• Yi X, Chen Q, Yang J, et al. CT-Based sarcopenic nomogram for predicting progressive disease in advanced non-small-cell lung cancer. Front Oncol. 2021;11:643941. doi:10.3389/fonc.2021.643941
   PubMed Web of Science ®Google Scholar
• de Jong C, Chargi N, Herder GJM, et al. The association between skeletal muscle measures and chemotherapy-induced toxicity in non-small cell lung cancer patients. J Cachexia Sarcope Musc. 2022;13(3):1554–1564. doi:10.1002/jcsm.12967
   PubMed Web of Science ®Google Scholar
• Kazemi-Bajestani SM, Mazurak VC, Baracos V. Computed tomography-defined muscle and fat wasting are associated with cancer clinical outcomes. Semin Cell Dev Biol. 2016;54:2–10. doi:10.1016/j.semcdb.2015.09.001
   PubMed Web of Science ®Google Scholar
• Sjøblom B, Grønberg BH, Wentzel-Larsen T, et al. Skeletal muscle radiodensity is prognostic for survival in patients with advanced non-small cell lung cancer. Clin Nutr. 2016;35(6):1386–1393. doi:10.1016/j.clnu.2016.03.010
   PubMed Web of Science ®Google Scholar
• Oruc Z, Akbay A, Ali Kaplan M, et al. A low body fat mass ratio predicts poor prognosis in patients with advanced non-small cell lung cancer. Nutr Cancer. 2022;74(9):3284–3291. doi:10.1080/01635581.2022.2074064
   PubMed Web of Science ®Google Scholar
• Gezer NS, Bandos AI, Beeche CA, Leader JK, Dhupar R, Pu J. CT-derived body composition associated with lung cancer recurrence after surgery. Lung Cancer. 2023;179:107189. doi:10.1016/j.lungcan.2023.107189
   PubMed Web of Science ®Google Scholar
• Zhang Y, Tan W, Zheng Z, Wang J, Xing L, Sun X. Body composition and radiomics from 18F-FDG PET/CT together help predict prognosis for patients with stage IV non-small cell lung cancer. J Comput Assist Tomogra. 2023;47(6):906–912. doi:10.1097/RCT.0000000000001496
   PubMed Web of Science ®Google Scholar