CoChat
Sign Up
Cited by 99

Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

Quanyang, Wu, Yao, Huang, Sicong, Wang, Linlin, Qi, Zewei, Zhang, Donghui, Hou, Hongjia, Li, Shijun, Zhao·2024·Cancer Medicine
DOI 10.1002/cam4.7140PMC PMC10997848
Open this paper in CoChat — free account

Abstract

Background: The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false‐positive reduction, nodule classification, and prognosis. Methodology: This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false‐positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening. Results: AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false‐positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing. Conclusions: AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false‐positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large‐scale validation of new deep learning‐based algorithms and multi‐center studies to improve the efficacy of AI in lung cancer screening.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/cam4.7140

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • AI can reduce inter-reader variability, improve consistency in nodule measurement, and assist in nodule characterization, potentially decreasing unnecessary work-up of benign nodules and expediting diagnosis of malignant ones.

    “AI can reduce inter-reader variability, improve consistency in nodule measurement, and assist in nodule characterization, potentially decreasing unnecessary work-up of benign nodules and expediting diagnosis of malignant ones [15].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [15]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 115

  1. [1]Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021; 71: 209‐ 249. doi: 10.3322/caac.21660 33538338 · DOI · Paper page
  2. [2]Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022; 72: 7‐ 33. doi: 10.3322/caac.21708 35020204 · DOI · Paper page
  3. [3]Ettinger DS. Ten years of progress in non‐small cell lung cancer. J Natl Compr Cancer Netw. 2012; 10: 292‐ 295. doi: 10.1200/JCO.2011.39.8594 22393190 · DOI
  4. [4]Gettinger S, Horn L, Jackman D, et al. Five‐year follow‐up of nivolumab in previously treated advanced non–small‐cell lung cancer: results from the CA209‐003 study. J Clin Oncol. 2018; 36: 1675‐ 1684. doi: 10.1200/JCO.2017.77.0412 29570421 · DOI
  5. [5]International Early Lung Cancer Action Program Investigators. Survival of patients with stage I lung cancer detected on CT screening. N Engl J Med. 2006; 355: 1763‐ 1771. doi: 10.1056/NEJMoa060476 17065637 · DOI
  6. [7]Yousaf‐Khan U, van der Aalst C, de Jong PA, et al. Final screening round of the NELSON lung cancer screening trial: the effect of a 2.5‐year screening interval. Thorax. 2017; 72: 48‐ 56. doi: 10.1136/thoraxjnl-2016-208655 27364640 · DOI
  7. [8]Cellina M, Cacioppa LM, Cè M, et al. Artificial intelligence in lung cancer screening: the future is now. Cancers (Basel). 2023; 15(17): 4344. doi: 10.3390/cancers15174344 37686619 PMC10486721 · DOI
  8. [9]Walter JE, Heuvelmans MA, Oudkerk M. Small pulmonary nodules in baseline and incidence screening rounds of low‐dose CT lung cancer screening. Transl Lung Cancer Res. 2017; 6: 42‐ 51. doi: 10.21037/tlcr.2016.11.05 28331823 PMC5344845 · DOI
  9. [10]Swensen SJ, Silverstein MD, Ilstrup DM, Schleck CD, Edell ES. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med. 1997; 157: 849‐ 855. 9129544
  10. [11]Reid M, Choi HK, Han X, et al. Development of a risk prediction model to estimate the probability of malignancy in pulmonary nodules being considered for biopsy. Chest. 2019; 156: 367‐ 375. doi: 10.1016/j.chest.2019.01.038 30940455 · DOI
  11. [12]Qi LL, Wu BT, Tang W, et al. Long‐term follow‐up of persistent pulmonary pure ground‐glass nodules with deep learning‐assisted nodule segmentation. Eur Radiol. 2020; 30: 744‐ 755. doi: 10.1007/s00330-019-06344-z 31485837 · DOI
  12. [13]de Margerie‐Mellon C, Ngo LH, Gill RR, et al. The growth rate of subsolid lung adenocarcinoma nodules at chest CT. Radiology. 2020; 297: 189‐ 198. doi: 10.1148/radiol.2020192322 32749206 · DOI
  13. [14]Kantarjian H, Yu PP. Artificial intelligence, big data, and cancer. JAMA Oncol. 2015; 1: 573‐ 574. doi: 10.1001/jamaoncol.2015.1203 26181906 · DOI
  14. [15]Tanaka I, Furukawa T, Morise M. The current issues and future perspective of artificial intelligence for developing new treatment strategy in non‐small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence. Cancer Cell Int. 2021; 21: 454. doi: 10.1186/s12935-021-02165-7 34446006 PMC8393743 · DOI
  15. [16]Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019; 380: 1347‐ 1358. doi: 10.1056/NEJMra1814259 30943338 · DOI · Paper page
  16. [17]Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521: 436‐ 444. doi: 10.1038/nature14539 26017442 · DOI · Paper page
  17. [18]Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non‐small cell lung cancer histopathology images using deep learning. Nat Med. 2018; 24: 1559‐ 1567. doi: 10.1038/s41591-018-0177-5 30224757 PMC9847512 · DOI · Paper page
  18. [20]Chan HP, Samala RK, Hadjiiski LM, Zhou C. Deep learning in medical image analysis. Adv Exp Med Biol. 2020; 1213: 3‐ 21. doi: 10.1007/978-3-030-33128-3_1 32030660 PMC7442218 · DOI
  19. [21]Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S. Emerging applications of artificial intelligence in neuro‐oncology. Radiology. 2019; 290: 607‐ 618. doi: 10.1148/radiol.2018181928 30667332 PMC6389268 · DOI
  20. [22]Nakaura T, Higaki T, Awai K, Ikeda O, Yamashita Y. A primer for understanding radiology articles about machine learning and deep learning. Diagn Interv Imaging. 2020; 101: 765‐ 770. doi: 10.1016/j.diii.2020.10.001 33121910 · DOI
  21. [23]Blanc D, Racine V, Khalil A, et al. Artificial intelligence solution to classify pulmonary nodules on CT. Diagn Interv Imaging. 2020; 101: 803‐ 810. doi: 10.1016/j.diii.2020.10.004 33168496 · DOI
  22. [24]Lu L, Tan Y, Schwartz LH, Zhao B. Hybrid detection of lung nodules on CT scan images. Med Phys. 2015; 42: 5042‐ 5054. doi: 10.1118/1.4927573 26328955 · DOI
  23. [25]Naqi SM, Sharif M, Yasmin M. Multistage segmentation model and SVM‐ensemble for precise lung nodule detection. Int J Comput Assist Radiol Surg. 2018; 13: 1083‐ 1095. doi: 10.1007/s11548-018-1715-9 29492880 · DOI
  24. [26]Krishnamurthy S, Narasimhan G, Rengasamy U. Three‐dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives. Proc Inst Mech Eng H. 2016; 230: 58‐ 70. doi: 10.1177/0954411915619951 26721427 · DOI
  25. [27]Khordehchi EA, Ayatollahi A, Daliri MR. Automatic lung nodule detection based on statistical region merging and support vector machines. Image Anal Stereol. 2017; 36: 65‐ 78. doi: 10.5566/ias.1679 · DOI
  26. [28]Gupta A, Saar T, Martens O, Moullec YL. Automatic detection of multisize pulmonary nodules in CT images: large‐scale validation of the false‐positive reduction step. Med Phys. 2018; 45: 1135‐ 1149. doi: 10.1002/mp.12746 29359462 · DOI
  27. [29]Gong J, Liu JY, Wang LJ, Sun XW, Zheng B, Nie SD. Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis. Phys Med. 2018; 46: 124‐ 133. doi: 10.1016/j.ejmp.2018.01.019 29519398 · DOI
  28. [30]Saien S, Hamid Pilevar A, Abrishami Moghaddam H. Refinement of lung nodule candidates based on local geometric shape analysis and Laplacian of gaussian kernels. Comput Biol Med. 2014; 54: 188‐ 198. doi: 10.1016/j.compbiomed.2014.09.010 25303113 · DOI
  29. [31]Han H, Li L, Han F, Song B, Moore W, Liang Z. Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme. IEEE J Biomed Health Inform. 2015; 19: 648‐ 659. doi: 10.1109/JBHI.2014.2328870 25486657 PMC4261060 · DOI
  30. [32]Shaukat F, Raja G, Gooya A, Frangi AF. Fully automatic detection of lung nodules in CT images using a hybrid feature set. Med Phys. 2017; 44: 3615‐ 3629. doi: 10.1002/mp.12273 28409834 · DOI
  31. [33]Li B, Chen K, Peng G, et al. Segmentation of ground glass opacity pulmonary nodules using an integrated active contour model with wavelet energy‐based adaptive local energy and posterior probability‐based speed function. Mater Express. 2016; 6: 317‐ 327. 10.1166/mex.2016.1311 · DOI
  32. [34]Shi Z, Ma J, Zhao M, et al. Many is better than one: an integration of multiple simple strategies for accurate lung segmentation in CT images. Biomed Res Int. 2016; 2016: 1480423. doi: 10.1155/2016/1480423 27635395 PMC5011243 · DOI
  33. [35]Soliman A, Khalifa F, Elnakib A, et al. Accurate lungs segmentation on CT chest images by adaptive appearance‐guided shape modeling. IEEE Trans Med Imaging. 2017; 36: 263‐ 276. doi: 10.1109/TMI.2016.2606370 27705854 · DOI
  34. [36]Rebouças Filho PP, Cortez PC, da Silva Barros AC, Albuquerque VH, Tavares JM. Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images. Med Image Anal. 2017; 35: 503‐ 516. doi: 10.1016/j.media.2016.09.002 27614793 · DOI
  35. [37]Zhang Y, Osanlouy M, Clark AR, et al. Pulmonary lobar segmentation from computed tomography scans based on a statistical finite element analysis of lobe shape. Medical imaging 2019: image processing. SPIE. 2019; 10949: 790‐ 799. doi: 10.1117/12.2512642 · DOI
  36. [38]Fischer AM, Varga‐Szemes A, Martin SS, et al. Artificial intelligence‐based fully automated per lobe segmentation and emphysema‐quantification based on chest computed tomography compared with global initiative for chronic obstructive lung disease severity of smokers. J Thorac Imaging. 2020; 35: S28‐ S34. doi: 10.1097/RTI.0000000000000500 32235188 · DOI
  37. [39]Park J, Yun J, Kim N, et al. Fully automated lung lobe segmentation in volumetric chest CT with 3D U‐net: validation with intra‐ and extra‐datasets. J Digit Imaging. 2020; 33: 221‐ 230. doi: 10.1007/s10278-019-00223-1 31152273 PMC7064651 · DOI
  38. [40]Zhang Z, Ren J, Tao X, et al. Automatic segmentation of pulmonary lobes on low‐dose computed tomography using deep learning. Ann Transl Med. 2021; 9: 291. doi: 10.21037/atm-20-5060 33708918 PMC7944332 · DOI
  39. [41]Liu J, Wang C, Guo J, et al. RPLS‐net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi‐task learning. Int J Comput Assist Radiol Surg. 2021; 16: 895‐ 904. doi: 10.1007/s11548-021-02360-x 33846890 · DOI
  40. [42]Pang H, Wu Y, Qi S, et al. A fully automatic segmentation pipeline of pulmonary lobes before and after lobectomy from computed tomography images. Comput Biol Med. 2022; 147: 105792. doi: 10.1016/j.compbiomed.2022.105792 35780601 · DOI
  41. [43]Filho AOC, Silva AC, de Paiva AC, Nunes RA, Gattass M. 3D shape analysis to reduce false positives for lung nodule detection systems. Med Biol Eng Comput. 2017; 55: 1199‐ 1213. doi: 10.1007/s11517-016-1582-x 27752930 · DOI
  42. [44]Wang J, Cheng Y, Guo C, Tamura S. A new pulmonary nodules detection scheme utilizing region grow and adaptive fuzzy c‐means clustering. J Med Imaging Health Inform. 2015; 5: 1941‐ 1946. doi: 10.1166/jmihi.2015.1674 · DOI
  43. [45]Cha J, Farhangi MM, Dunlap N, Amini AA. Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT. Med Phys. 2018; 45: 297‐ 306. doi: 10.1002/mp.12690 29164630 · DOI
  44. [46]Zheng S, Guo J, Cui X, Veldhuis RNJ, Oudkerk M, van Ooijen PMA. Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection. IEEE Trans Med Imaging. 2020; 39: 797‐ 805. doi: 10.1109/TMI.2019.2935553 31425026 · DOI
  45. [47]Dou Q, Chen H, Yu L, Qin J, Heng PA. Multilevel contextual 3‐D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng. 2017; 64: 1558‐ 1567. doi: 10.1109/TBME.2016.2613502 28113302 · DOI
  46. [48]Cellina M, Cè M, Irmici G, et al. Artificial intelligence in lung cancer imaging: unfolding the future. Diagnostics (Basel). 2022; 12(11): 2644. doi: 10.3390/diagnostics12112644 36359485 PMC9689810 · DOI
  47. [50]Zhu W, Liu C, Fan W, Xie X. Deeplung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE; 2018: 673‐ 681. doi: 10.1109/WACV.2018.00079 · DOI
  48. [51]Ozdemir O, Russell RL, Berlin AA. A 3D probabilistic deep learning system for detection and diagnosis of lung cancer using low‐dose CT scans. IEEE Trans Med Imaging. 2020; 39: 1419‐ 1429. doi: 10.1109/TMI.2019.2947595 31675322 · DOI
  49. [52]Li F, Huang H, Wu Y, et al. Lung Nodule Detection with a 3D ConvNet via IoU Self‐normalization and Maxout Unit. ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2019: 1214‐ 1218. doi: 10.1109/ICASSP.2019.8683537 · DOI
  50. [53]Zheng S, Cornelissen LJ, Cui X, et al. Deep convolutional neural networks for multiplanar lung nodule detection: improvement in small nodule identification. Med Phys. 2021; 48: 733‐ 744. doi: 10.1002/mp.14648 33300162 PMC7986069 · DOI
  51. [54]Liu J, Cao L, Akin O, et al. Accurate and Robust Pulmonary Nodule Detection by 3D Feature Pyramid Network with Self‐Supervised Feature Learning. arXiv; 2019 Available at: https://arxiv.org/abs/1907.11704 · arXiv
  52. [55]Wang D, Zhang Y, Zhang K, et al. Focalmix: Semi‐Supervised Learning for 3D Medical Image Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2020: 3951‐ 3960. doi: 10.1109/CVPR42600.2020.00401 · DOI · Paper page
  53. [56]Liao F, Liang M, Li Z, Hu X, Song S. Evaluate the malignancy of pulmonary nodules using the 3‐D deep leaky noisy‐OR network. IEEE Trans Neural Netw Learn Syst. 2019; 30: 3484‐ 3495. doi: 10.1109/TNNLS.2019.2892409 30794190 · DOI
  54. [57]Azad R, Asadi‐Aghbolaghi M, Fathy M, Escalera S. Bi‐Directional ConvLSTM U‐Net with Densley Connected Convolutions. Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops. arXiv; 2019. doi: 10.48550/arXiv.1909.00166 · DOI
  55. [58]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: 46479. doi: 10.1038/srep46479 [Published correction appears in sci rep. 2017 Sep 07;7:46878]. 28880026 PMC5588055 · DOI
  56. [59]Cai L, Long T, Dai Y, Huang Y. Mask R‐CNN‐based detection and segmentation for pulmonary nodule 3D visualization diagnosis. IEEE Access. 2020; 8: 44400‐ 44409. doi: 10.1109/ACCESS.2020.2976432 · DOI
  57. [60]Ren Y, Tsai MY, Chen L, et al. A manifold learning regularization approach to enhance 3D CT image‐based lung nodule classification. Int J Comput Assist Radiol Surg. 2020; 15: 287‐ 295. doi: 10.1007/s11548-019-02097-8 31768885 · DOI
  58. [61]Nasrullah N, Sang J, Alam MS, Mateen M, Cai B, Hu H. Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors. 2019; 19: 3722. doi: 10.3390/s19173722 31466261 PMC6749467 · DOI
  59. [62]Yuan H, Wu Y, Dai M. Multi‐modal feature fusion‐based multi‐branch classification network for pulmonary nodule malignancy suspiciousness diagnosis. J Digit Imaging. 2023; 36: 617‐ 626. doi: 10.1007/s10278-022-00747-z 36478311 PMC10039149 · DOI
  60. [63]Schreuder A, Scholten ET, van Ginneken B, Jacobs C. Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice? Transl Lung Cancer Res. 2021; 10: 2378‐ 2388. doi: 10.21037/tlcr-2020-lcs-06 34164285 PMC8182724 · DOI
  61. [64]Bergtholdt M, Wiemker R, Klinder T. Pulmonary nodule detection using a cascaded SVM classifier. Medical imaging 2016: computer‐aided diagnosis. Vol 9785. SPIE; 2016: 268‐ 278. doi: 10.1117/12.2216747 · DOI
  62. [65]Jacobs C, van Rikxoort EM, Twellmann T, et al. Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med Image Anal. 2014; 18: 374‐ 384. doi: 10.1016/j.media.2013.12.001 24434166 · DOI
  63. [66]Teramoto A, Fujita H. Automated lung nodule detection using positron emission tomography/computed tomography. Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging. Springer; 2018: 87‐ 110. doi: 10.1007/978-3-319-68843-5_4 · DOI
  64. [67]Tartar A, Kilic N, Akan A. Classification of pulmonary nodules by using hybrid features. Comput Math Methods Med. 2013; 2013: 148363. doi: 10.1155/2013/148363 23970942 PMC3708407 · DOI
  65. [68]Kim BC, Yoon JS, Choi JS, Suk HI. Multi‐scale gradual integration CNN for false positive reduction in pulmonary nodule detection. Neural Netw. 2019; 115: 1‐ 10. doi: 10.1016/j.neunet.2019.03.003 30909118 · DOI
  66. [69]Zuo W, Zhou F, He Y. An embedded multi‐branch 3D convolution neural network for false positive reduction in lung nodule detection. J Digit Imaging. 2020; 33: 846‐ 857. doi: 10.1007/s10278-020-00326-0 32095944 PMC7522146 · DOI
  67. [70]Masood A, Yang P, Sheng B, et al. Cloud‐based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT. IEEE J Transl Eng Health Med. 2019; 8: 4300113. doi: 10.1109/JTEHM.2019.2955458 31929952 PMC6946021 · DOI
  68. [71]Yuan H, Fan Z, Wu Y, Cheng J. An efficient multi‐path 3D convolutional neural network for false‐positive reduction of pulmonary nodule detection. Int J Comput Assist Radiol Surg. 2021; 16: 2269‐ 2277. doi: 10.1007/s11548-021-02478-y 34449037 · DOI
  69. [72]Savitha G, Jidesh P. A fully‐automated system for identification and classification of subsolid nodules in lung computed tomographic scans. Biomed Signal Process Control. 2019; 53: 101586. doi: 10.1016/j.bspc.2019.101586 · DOI
  70. [73]Paul R, Hall L, Goldgof D, Schabath M, Gillies R. Predicting nodule malignancy using a CNN ensemble approach. Proc Int Jt Conf Neural Netw. 2018; 2018: 8489345. doi: 10.1109/IJCNN.2018.8489345 PMC6233309 30443438 · DOI
  71. [74]Nishio M, Sugiyama O, Yakami M, et al. Computer‐aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. PLoS One. 2018; 13: e0200721. doi: 10.1371/journal.pone.0200721 30052644 PMC6063408 · DOI
  72. [75]Al‐Shabi M, Lee HK, Tan M. Gated‐dilated networks for lung nodule classification in CT scans. IEEE Access. 2019; 7: 178827–178838. doi: 10.1109/ACCESS.2019.2958663 · DOI
  73. [76]Apostolopoulos ID. Experimenting with Convolutional Neural Network Architectures for the Automatic Characterization of Spulmonary Nodules' Malignancy Rating. arXiv; 2020. doi: 10.48550/arXiv.2003.06801 · DOI
  74. [77]de Carvalho Filho AO, Silva AC, de Paiva AC, Nunes RA, Gattass M. Classification of patterns of benignity and malignancy based on CT using topology‐based phylogenetic diversity index and convolutional neural network. Pattern Recogn. 2018; 81: 200‐ 212. doi: 10.1016/j.patcog.2018.03.032 · DOI
  75. [78]Xie Y, Xia Y, Zhang J, et al. Knowledge‐based collaborative deep learning for benign‐malignant lung nodule classification on chest CT. IEEE Trans Med Imaging. 2019; 38: 991‐ 1004. doi: 10.1109/TMI.2018.2876510 30334786 · DOI
  76. [79]Liu L, Dou Q, Chen H, Qin J, Heng PA. Multi‐task deep model with margin ranking loss for lung nodule analysis. IEEE Trans Med Imaging. 2020; 39: 718‐ 728. doi: 10.1109/TMI.2019.2934577 31403410 · DOI
  77. [80]Bonavita I, Rafael‐Palou X, Ceresa M, Piella G, Ribas V, González Ballester MA. Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline. Comput Methods Prog Biomed. 2020; 185: 105172. doi: 10.1016/j.cmpb.2019.105172 31710985 · DOI
  78. [81]Harsono IW, Liawatimena S, Cenggoro TW. Lung nodule detection and classification from thorax CT‐scan using RetinaNet with transfer learning. J King Saud Univ Comput Inf Sci. 2022; 34: 567‐ 577. doi: 10.1016/j.jksuci.2020.03.013 · DOI
  79. [82]Guo Y, Song Q, Jiang M, et al. Histological subtypes classification of lung cancers on CT images using 3D deep learning and radiomics. Acad Radiol. 2021; 28: e258‐ e266. doi: 10.1016/j.acra.2020.06.010 32622740 · DOI
  80. [83]Kim H, Goo JM, Lee KH, Kim YT, Park CM. Preoperative CT‐based deep learning model for predicting disease‐free survival in patients with lung adenocarcinomas. Radiology. 2020; 296: 216‐ 224. doi: 10.1148/radiol.2020192764 32396042 · DOI
  81. [84]Shimada Y, Kudo Y, Maehara S, et al. Radiomics with artificial intelligence for the prediction of early recurrence in patients with clinical stage IA lung cancer. Ann Surg Oncol. 2022; 29: 8185‐ 8193. doi: 10.1245/s10434-022-12516-x 36070112 · DOI
  82. [85]Wang S, Yu H, Gan Y, et al. Mining whole‐lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. Lancet Digit Health. 2022; 4: e309‐ e319. doi: 10.1016/S2589-7500(22)00024-3 35341713 · DOI
  83. [86]Wang S, Shi J, Ye Z, et al. Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. Eur Respir J. 2019; 53: 1800986. doi: 10.1183/13993003.00986-2018 30635290 PMC6437603 · DOI
  84. [87]Peled N, Ilouze M. Screening for lung cancer: what comes next? J Clin Oncol. 2015; 33(33): 3847‐ 3848. doi: 10.1200/JCO.2015.63.1713 26304887 · DOI
  85. [88]Sullivan FM, Mair FS, Anderson W, et al. Earlier diagnosis of lung cancer in a randomised trial of an autoantibody blood test followed by imaging. Eur Respir J. 2021; 57(1): 2000670. doi: 10.1183/13993003.00670-2020 32732334 PMC7806972 · DOI
  86. [89]Montani F, Marzi MJ, Dezi F, et al. miR‐test: a blood test for lung cancer early detection. J Natl Cancer Inst. 2015; 107(6): djv063. doi: 10.1093/jnci/djv063 25794889 · DOI
  87. [90]Causey JL, Zhang J, Ma S, et al. Highly accurate model for prediction of lung nodule malignancy with CT scans. Sci Rep. 2018; 8: 9286. doi: 10.1038/s41598-018-27569-w 29915334 PMC6006355 · DOI
  88. [91]Dey R, Lu Z, Hong Y. Diagnostic Classification of Lung Nodules Using 3D Neural Networks. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE; 2018: 774‐ 778. doi: 10.1109/ISBI.2018.8363687 · DOI
  89. [92]Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad‐CAM: visual explanations from deep networks via gradient‐based localization. 2017 IEEE International Conference on Computer Vision (ICCV). IEEE Xplore; 2017: 618‐ 626. doi: 10.1109/ICCV.2017.74 · DOI · Paper page
  90. [94]Han C, Kitamura Y, Kudo A, et al. Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi‐Conditional GAN‐Based CT Image Augmentation for Object Detection. 2019 International Conference on 3D Vision (3DV). IEEE; 2019: 729‐ 737.
  91. [95]Zhou Z, Sodha V, Siddiquee MMR, et al. Models genesis: generic autodidactic models for 3D medical image analysis. Med Image Comput Comput Assist Interv. 2019; 11767: 384‐ 393. doi: 10.1007/978-3-030-32251-9_42 32766570 PMC7405596 · DOI
  92. [96]Hussein S, Kandel P, Bolan CW, Wallace MB, Bagci U. Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches. IEEE Trans Med Imaging. 2019; 38: 1777‐ 1787. doi: 10.1109/TMI.2019.2894349 30676950 · DOI · Paper page
  93. [97]Kawagishi M, Kubo T, Sakamoto R, et al. Automatic inference model construction for computer‐aided diagnosis of lung nodule: explanation adequacy, inference accuracy, and experts' knowledge. PLoS One. 2018; 13: e0207661. doi: 10.1371/journal.pone.0207661 30444907 PMC6239329 · DOI
  94. [98]Liu B, Chi W, Li X, et al. Evolving the pulmonary nodules diagnosis from classical approaches to deep learning‐aided decision support: three decades' development course and future prospect. J Cancer Res Clin Oncol. 2020; 146: 153‐ 185. doi: 10.1007/s00432-019-03098-5 31786740 PMC11804672 · DOI
  95. [99]Dong X, Xu S, Liu Y, et al. Multi‐view secondary input collaborative deep learning for lung nodule 3D segmentation. Cancer Imaging. 2020; 20: 53. doi: 10.1186/s40644-020-00331-0 32738913 PMC7395980 · DOI
  96. [100]Cui S, Ming S, Lin Y, et al. Development and clinical application of deep learning model for lung nodules screening on CT images. Sci Rep. 2020; 10: 13657. doi: 10.1038/s41598-020-70629-3 32788705 PMC7423892 · DOI
  97. [102]Yu H, Li J, Zhang L, Cao Y, Yu X, Sun J. Design of lung nodules segmentation and recognition algorithm based on deep learning. BMC Bioinformatics. 2021; 22: 314. doi: 10.1186/s12859-021-04234-0 34749636 PMC8576909 · DOI
  98. [103]Liu D, Liu F, Tie Y, Qi L, Wang F. Res‐trans networks for lung nodule classification. Int J Comput Assist Radiol Surg. 2022; 17: 1059‐ 1068. doi: 10.1007/s11548-022-02576-5 35290646 · DOI
  99. [104]Khan A, Tariq I, Khan H, et al. Lung cancer nodules detection via an adaptive boosting algorithm based on self‐normalized multiview convolutional neural network. J Oncol. 2022; 2022: 5682451. doi: 10.1155/2022/5682451 36199795 PMC9529389 · DOI
  100. [105]Liu W, Liu X, Luo X, et al. A pyramid input augmented multi‐scale CNN for GGO detection in 3D lung CT images. Pattern Recogn. 2023; 136: 109261. doi: 10.1016/j.patcog.2022.109261 · DOI
  101. [106]Siddiqui EA, Chaurasia V, Shandilya M. Classification of lung cancer computed tomography images using a 3‐dimensional deep convolutional neural network with multi‐layer filter. J Cancer Res Clin Oncol. 2023; 149(13): 11279‐ 11294. doi: 10.1007/s00432-023-04992-9 37368121 PMC11797377 · DOI
  102. [107]Teramoto A, Fujita H, Takahashi K, et al. Hybrid method for the detection of pulmonary nodules using positron emission tomography/computed tomography: a preliminary study. Int J Comput Assist Radiol Surg. 2014; 9: 59‐ 69. doi: 10.1007/s11548-013-0910-y 23793722 · DOI
  103. [108]Zuo W, Zhou F, Li Z, Wang L. Multi‐resolution CNN and knowledge transfer for candidate classification in lung nodule detection. IEEE Access. 2019; 7: 32510‐ 32521. doi: 10.1109/ACCESS.2019.2903587 · DOI
  104. [109]Sun L, Wang Z, Pu H, et al. Spectral analysis for pulmonary nodule detection using the optimal fractional S‐transform. Comput Biol Med. 2020; 119: 103675. doi: 10.1016/j.compbiomed.2020.103675 32339120 · DOI
  105. [110]Majidpourkhoei R, Alilou M, Majidzadeh K, Babazadehsangar A. A novel deep learning framework for lung nodule detection in 3D CT images. Multimed Tools Appl. 2021; 80: 30539‐ 30555. doi: 10.1007/s11042-021-11066-w · DOI
  106. [111]Mkindu H, Wu L, Zhao Y. Lung nodule detection of CT images based on combining 3D‐CNN and squeeze‐and‐excitation networks. Multimed Tools Appl. 2023; 82: 25747‐ 25760. doi: 10.1007/s11042-023-14581-0 · DOI
  107. [112]Petousis P, Han SX, Aberle D, Bui AA. Prediction of lung cancer incidence on the low‐dose computed tomography arm of the National Lung Screening Trial: a dynamic Bayesian network. Artif Intell Med. 2016; 72: 42‐ 55. doi: 10.1016/j.artmed.2016.07.001 27664507 PMC5082434 · DOI
  108. [113]Balagurunathan Y, Schabath MB, Wang H, Liu Y, Gillies RJ. Quantitative imaging features improve discrimination of malignancy in pulmonary nodules. Sci Rep. 2019; 9: 8528. doi: 10.1038/s41598-019-44562-z 31189944 PMC6561979 · DOI
  109. [114]Al‐Shabi M, Lan BL, Chan WY, Ng KH, Tan M. Lung nodule classification using deep local‐global networks. Int J Comput Assist Radiol Surg. 2019; 14: 1815‐ 1819. doi: 10.1007/s11548-019-01981-7 31020576 · DOI
  110. [115]Chen S, Ma K, Zheng Y. Med3d: Transfer Learning for 3D Medical Image Analysis. arXiv; 2019 Available at: https://arxiv.org/abs/1904.00625 · arXiv
  111. [116]Yang J, Deng H, Huang X, et al. Relational Learning Between Multiple Pulmonary Nodules via Deep Set Attention Transformers. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE; 2020: 1875‐ 1878. doi: 10.1109/ISBI45749.2020.9098722 · DOI
  112. [117]Halder A, Chatterjee S, Dey D. Adaptive morphology aided 2‐pathway convolutional neural network for lung nodule classification. Biomedical Signal Processing and Control. 2022; 72: 103347. doi: 10.1016/j.bspc.2021.103347 · DOI
  113. [118]Mikhael PG, Wohlwend J, Yala A, et al. Sybil: a validated deep learning model to predict future lung cancer risk from a single low‐dose chest computed tomography. J Clin Oncol. 2023; 41: 2191‐ 2200. doi: 10.1200/JCO.22.01345 36634294 PMC10419602 · DOI · Paper page
  114. [119]Bushara AR, Vinod Kumar RS, Kumar SS. LCD‐capsule network for the detection and classification of lung cancer on computed tomography images. Multimed Tools Appl. 2023; 82: 1‐ 20. doi: 10.1007/s11042-023-14893-1 · DOI
  115. [120]Irshad RR, Hussain S, Sohail SS, et al. A novel IoT‐enabled healthcare monitoring framework and improved Grey wolf optimization algorithm‐based deep convolution neural network model for early diagnosis of lung cancer. Sensors (Basel). 2023; 23(6): 2932. doi: 10.3390/s23062932 36991642 PMC10052730 · DOI

Cited by

Sorted by citations

Authors
Quanyang, W., Yao, H., Sicong, W., Linlin, Q., Zewei, Z., Donghui, H., Hongjia, L., & Shijun, Z.
Affiliations
  • Department of Diagnostic Radiology National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China
  • Magnetic Resonance Imaging Research General Electric Healthcare (China) Beijing China
  • PET‐CT Center National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China
ORCID
Shijun, Zhao
Venue
Cancer Medicine
Publisher
Wiley
Published
2024-04
Volume
13
Issue
7
ISSN
2045-7634
DOI
10.1002/cam4.7140
PMC
PMC10997848
Cited by
99
References
5
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor, 4 d)
Funders
National Key Research and Development Program of China (2020AAA0109504)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review