Research progress of auxiliary diagnosis classification algorithm for lung tumor imaging_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

CUI Dongmei ,  WEI Guohui , ZHANG Xike ,  MA Zhiqing

College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, 250355, P. R. China;

Corresponding author：

WEI Guohui, Email: bmie530@163.com; MA Zhiqing, Email: mazhq126@163.com

Keywords：

Lung tumor classification; deep learning; radiomics; multiple data fusion; model fusion; artificial intelligence; review

DOI：

10.7507/1007-4848.202201049

Video：

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Abstract Full text Figures/Tables Video References Cited by

The classification of lung tumor with the help of computer-aided diagnosis system is very important for the early diagnosis and treatment of malignant lung tumors. At present, the main research direction of lung tumor classification is the model fusion technology based on deep learning, which classifies the multiple fusion data of lung tumor with the help of radiomics. This paper summarizes the commonly used research algorithms for lung tumor classification, introduces concepts and technologies of machine learning, radiomics, deep learning and multiple data fusion, points out the existing problems and difficulties in the field of lung tumor classification, and looks forward to the development prospect and future research direction of lung tumor classification.

Citation： CUI Dongmei, WEI Guohui, ZHANG Xike, MA Zhiqing. Research progress of auxiliary diagnosis classification algorithm for lung tumor imaging. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(7): 892-898. doi: 10.7507/1007-4848.202201049 Copy

1.	巨娟, 林檬, 曾祥飞, 等. 基于CT影像智能分析诊断早期肺癌的最新研究进展. 中国胸心血管外科临床杂志, 2021, 28(3): 354-357.
2.	Kozuka T, Matsukubo Y, Kadoba T, et al. Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography. Jpn J Radiol, 2020, 38(11): 1052-1061.
3.	魏一娟, 潘宁, 陈岩, 等. 深度学习辅助诊断系统在胸片的应用研究: 气胸及肺结节检测. 临床放射学杂志, 2021, 40(2): 252-257.
4.	尹柯, 张久权, 伍建林, 等. 对比卷积神经网络分类模型与放射科医师鉴别浸润性肺腺癌的效能. 中国医学影像技术, 2021, 37(9): 1338-1342.
5.	Bhat S, Shashikala R, Kumar S, et al. Convolutional neural network approach for the classification and recognition of lung nodules. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2020: 1310-1314.
6.	Nadkarni NS, Borkar S. Detection of lung cancer in CT images using image processing. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019: 863-866.
7.	Li M, Ma XJ, Chen C, et al. Research on the auxiliary classification and diagnosis of lung cancer subtypes based on histopathological images. IEEE Access, 2021, 9: 53687-53707.
8.	Gopi K, Selvakumar J. Lung tumor area recognition and classification using EK-mean clustering and SVM. International Conference on Nextgen Electronic Technologies: Silicon to Software, 2017: 97-100.
9.	Easwaran U, Kandasamy Y, Chellappan R, et al. Impact of biomaterials in lung tumor classification and segmentation using machine learning healthcare. Mater Today Proc, 2021, 43(5): 3100-3104.
10.	Patel V, Shah S, Trivedi H, et al. An analysis of lung tumor classification using SVM and ANN with GLCM features. In: Singh P, Pawłowski W, Tanwar S, et al. eds. Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019). Singapore: Springer, 2020.
11.	Potghan S, Rajamenakshi R, Bhise A. Multi-layer perceptron based lung tumor classification. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018.
12.	何校栋, 邢海群, 王瞳, 等. 基于Adaboost算法的多特征融合肺部PET-CT图像的肿瘤分类方法. 中国医学装备, 2017, 14(8): 5-10.
13.	Sarker P, Shuvo M, Hossain Z, et al. Segmentation and classification of lung tumor from 3D CT image using K-means clustering algorithm. 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), IEEE, 2017.
14.	Agrawal VL, Dudul SV. Conventional neural network approach for the diagnosis of lung tumor. 2020 International Conference on Computational Performance Evaluation (ComPE), 2020: 543-547.
15.	Azzawi H, Hou J, Alnnni R, et al. SBC: A new strategy for multiclass lung cancer classification based on tumour structural information and microarray data. 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), 2018: 68-73.
16.	Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
17.	杨培伟, 周余红, 邢岗, 等. 卷积神经网络在生物医学图像上的应用进展. 计算机工程与应用, 2021, 57(7): 44-58.
18.	Wang YW, Chen CC, Wang TC, et al. Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning. Comput Biol Med, 2022, 141: 105185.
19.	Mukherjee S, Bohra SU. Lung cancer disease diagnosis using machine learning approach. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), 2020: 207-211.
20.	Abdul W. An automatic lung cancer detection and classification (ALCDC) system using convolutional neural network. 2020 13th International Conference on Developments in eSystems Engineering (DeSE), 2020: 443-446.
21.	Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
22.	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(10): 1559-1567.
23.	Kasinathan G, Jayakumar S, Gandomi AH, et al. Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier. Exp Sys Appl, 2019, 134: 112-119.
24.	Agarwal A, Patni K, Rajeswari D. Lung cancer detection and classification based on Alexnet CNN. 2021 6th International Conference on Communication and Electronics Systems (ICCES), 2021: 1390-1397.
25.	Ss A, Sm B. NROI based feature learning for automated tumor stage classification of pulmonary lung nodules using deep convolutional neural networks. Comp Inform Sci, 2022, 35(4): 1706-1717.
26.	Mohanapriya N, Kalaavathi B, Kuamr TS. Lung tumor classification and detection from CT scan images using deep convolutional neural networks (DCNN). 2019 International Conference on Computational Intelligence and Knowledge Economy, 2019: 800-805.
27.	李斌, 李科宇, 汤渝玲, 等. 基于深度学习的肺癌计算机辅助诊断. 当代医学, 2021, 27(9): 89-93.
28.	Gong J, Liu J, Hao W, et al. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. Eur Radiol, 2020, 30(4): 1847-1855.
29.	Xu X, Hou R, Zhao W, et al. A weak supervision-based framework for automatic lung cancer classification on whole slide image. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020.
30.	史张, 刘崎. 影像组学技术方法的研究及挑战. 放射学实践, 2018, 33(6): 633-636.
31.	Kotrotsou A, Zinn PO, Colen RR. Radiomics in brain tumors: An emerging technique for characterization of tumor environment. Magn Reson Imaging Clin N Am, 2016, 24(4): 719-729.
32.	Wang J, Liu X, Dong D, et al. Prediction of malignant and benign of lung tumor using a quantitative radiomic method. Annu Int Conf IEEE Eng Med Biol Soc, 2016, 2016: 1272-1275.
33.	余烨, 吴华伟. 影像组学在肺癌中的应用进展. 国际医学放射学杂志, 2018, 41(6): 646-649.
34.	陈震东. 基于影像组学的肺肿瘤良恶性分类及早期肺腺癌淋巴结转移预测模型研究. 浙江师范大学, 2018.
35.	周天绮, 朱超挺, 石峰. 影像组学在肺肿瘤良恶性分类预测中的应用研究. 中国医疗器械杂志, 2020, 44(2): 113-117.
36.	黄志成, 叶钉利, 胡乔治, 郑君, 赵瑞坤. 基于CT影像组学模型鉴别诊断小细胞肺癌与非小细胞肺癌. 中国介入影像与治疗学, 2021, 18(8): 474-478.
37.	石镇维, 刘再毅. 影像组学研究的困境和出路. 中华放射学杂志, 2022, 56(1): 9-11.
38.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
39.	李雪, 周金治, 莫春梅, 等. 基于特征融合的U-Net肺自动分割方法. 中国医学物理学杂志, 2021, 38(6): 704-712.
40.	王生生, 王琪. 融合LBP和小波矩特征的肺癌图像精细分类. 东北师大学报(自然科学版), 2017, 49(2): 57-63.
41.	Luo Z, Brubaker M A, Brudno M. Size and texture-based classification of lung tumors with 3D CNNs. Applications of Computer Vision. IEEE, 2017.
42.	郑德重. 基于多模态数据融合的肺部肿瘤智能分析技术研究. 中国科学院大学(中国科学院上海技术物理研究所), 2021.
43.	Li L, Lu W, Tan S. Variational PET/CT tumor co-segmentation integrated with PET restoration. IEEE Trans Radiat Plasma Med Sci, 2020, 4(1): 37-49.
44.	张飞飞, 周涛, 陆惠玲, 等. 基于集成VPRS-RUGGA支持向量机的多模态肺部肿瘤计算机辅助诊断模型. 生物医学工程研究, 2019, 38(1): 48-53.
45.	吴翠颖, 周涛, 陆惠玲, 等. 基于集成SVM的肺部肿瘤PET/CT三模态计算机辅助诊断方法. 生物医学工程研究, 2017, 36(3): 207-212.
46.	梁蒙蒙. 基于卷积神经网络的多模态医学图像分类研究. 宁夏医科大学, 2019.
47.	武志远, 马圆, 唐浩, 等. 基于深度卷积神经网络方法构建肺部多模态图像分类诊断模型. 中国卫生统计, 2019, 36(6): 806-808, 813.
48.	王蕊芳. 基于卷积神经网络的多模态医学图像融合方法研究. 中北大学, 2021.
49.	王媛媛, 周涛, 陆惠玲, 等. 基于集成卷积神经网络的肺部肿瘤计算机辅助诊断模型. 生物医学工程学杂志, 2017, 34(4): 543-551.
50.	邬雪涛, 林岚, 王婧璇. 基于CT图像的肺实质分割技术研究进展. 智慧健康, 2019, 5(20): 87-89.
51.	石邈, 续力云, 潘鑫福, 等. 三维重建技术在肺腺癌新分类标准诊断中的价值. 中国胸心血管外科临床杂志, 2021, 28(3): 278-282.
52.	孙翎马. 肺部CT影像智能分析及辅助诊断关键技术研究. 电子科技大学, 2021.
53.	张紫程. 基于肺癌PET/CT影像的诊断模型研究. 兰州大学, 2019.
54.	任海玲, 周涛, 霍兵强. 基于集成DE-NRS的肺部肿瘤影像组学计算机辅助诊断模型. 计算机应用与软件, 2020, 37(5): 156-163, 204.
55.	Hussein S, Kandel P, Bolan CW, et al. Lung and pancreatic tumor characterization in the deep learning era: Novel supervised and unsupervised learning approaches. IEEE Trans Med Imaging, 2019, 38(8): 1777-1787.
56.	Naik A, Edla DR. Lung tumor classification using CNN- and GLCM-based features. 2021.
57.	霍兵强, 周涛, 陆惠玲, 等. 基于NRC和多模态残差神经网络的肺部肿瘤良恶性分类. 山东大学学报(工学版), 2020, 50(6): 59-67, 75.
58.	刘锐, 何先波. 基于深度学习的肺部医学图像分析研究进展. 川北医学院学报, 2019, 34(2): 316-320.
59.	韩光辉, 刘峡壁, 郑光远. 肺部CT图像病变区域检测方法. 自动化学报, 2017, 43(12): 2071-2090.

1. 巨娟, 林檬, 曾祥飞, 等. 基于CT影像智能分析诊断早期肺癌的最新研究进展. 中国胸心血管外科临床杂志, 2021, 28(3): 354-357.
2. Kozuka T, Matsukubo Y, Kadoba T, et al. Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography. Jpn J Radiol, 2020, 38(11): 1052-1061.
3. 魏一娟, 潘宁, 陈岩, 等. 深度学习辅助诊断系统在胸片的应用研究: 气胸及肺结节检测. 临床放射学杂志, 2021, 40(2): 252-257.
4. 尹柯, 张久权, 伍建林, 等. 对比卷积神经网络分类模型与放射科医师鉴别浸润性肺腺癌的效能. 中国医学影像技术, 2021, 37(9): 1338-1342.
5. Bhat S, Shashikala R, Kumar S, et al. Convolutional neural network approach for the classification and recognition of lung nodules. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2020: 1310-1314.
6. Nadkarni NS, Borkar S. Detection of lung cancer in CT images using image processing. 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019: 863-866.
7. Li M, Ma XJ, Chen C, et al. Research on the auxiliary classification and diagnosis of lung cancer subtypes based on histopathological images. IEEE Access, 2021, 9: 53687-53707.
8. Gopi K, Selvakumar J. Lung tumor area recognition and classification using EK-mean clustering and SVM. International Conference on Nextgen Electronic Technologies: Silicon to Software, 2017: 97-100.
9. Easwaran U, Kandasamy Y, Chellappan R, et al. Impact of biomaterials in lung tumor classification and segmentation using machine learning healthcare. Mater Today Proc, 2021, 43(5): 3100-3104.
10. Patel V, Shah S, Trivedi H, et al. An analysis of lung tumor classification using SVM and ANN with GLCM features. In: Singh P, Pawłowski W, Tanwar S, et al. eds. Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019). Singapore: Springer, 2020.
11. Potghan S, Rajamenakshi R, Bhise A. Multi-layer perceptron based lung tumor classification. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018.
12. 何校栋, 邢海群, 王瞳, 等. 基于Adaboost算法的多特征融合肺部PET-CT图像的肿瘤分类方法. 中国医学装备, 2017, 14(8): 5-10.
13. Sarker P, Shuvo M, Hossain Z, et al. Segmentation and classification of lung tumor from 3D CT image using K-means clustering algorithm. 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), IEEE, 2017.
14. Agrawal VL, Dudul SV. Conventional neural network approach for the diagnosis of lung tumor. 2020 International Conference on Computational Performance Evaluation (ComPE), 2020: 543-547.
15. Azzawi H, Hou J, Alnnni R, et al. SBC: A new strategy for multiclass lung cancer classification based on tumour structural information and microarray data. 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), 2018: 68-73.
16. Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507.
17. 杨培伟, 周余红, 邢岗, 等. 卷积神经网络在生物医学图像上的应用进展. 计算机工程与应用, 2021, 57(7): 44-58.
18. Wang YW, Chen CC, Wang TC, et al. Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning. Comput Biol Med, 2022, 141: 105185.
19. Mukherjee S, Bohra SU. Lung cancer disease diagnosis using machine learning approach. 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), 2020: 207-211.
20. Abdul W. An automatic lung cancer detection and classification (ALCDC) system using convolutional neural network. 2020 13th International Conference on Developments in eSystems Engineering (DeSE), 2020: 443-446.
21. Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
22. 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(10): 1559-1567.
23. Kasinathan G, Jayakumar S, Gandomi AH, et al. Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier. Exp Sys Appl, 2019, 134: 112-119.
24. Agarwal A, Patni K, Rajeswari D. Lung cancer detection and classification based on Alexnet CNN. 2021 6th International Conference on Communication and Electronics Systems (ICCES), 2021: 1390-1397.
25. Ss A, Sm B. NROI based feature learning for automated tumor stage classification of pulmonary lung nodules using deep convolutional neural networks. Comp Inform Sci, 2022, 35(4): 1706-1717.
26. Mohanapriya N, Kalaavathi B, Kuamr TS. Lung tumor classification and detection from CT scan images using deep convolutional neural networks (DCNN). 2019 International Conference on Computational Intelligence and Knowledge Economy, 2019: 800-805.
27. 李斌, 李科宇, 汤渝玲, 等. 基于深度学习的肺癌计算机辅助诊断. 当代医学, 2021, 27(9): 89-93.
28. Gong J, Liu J, Hao W, et al. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. Eur Radiol, 2020, 30(4): 1847-1855.
29. Xu X, Hou R, Zhao W, et al. A weak supervision-based framework for automatic lung cancer classification on whole slide image. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020.
30. 史张, 刘崎. 影像组学技术方法的研究及挑战. 放射学实践, 2018, 33(6): 633-636.
31. Kotrotsou A, Zinn PO, Colen RR. Radiomics in brain tumors: An emerging technique for characterization of tumor environment. Magn Reson Imaging Clin N Am, 2016, 24(4): 719-729.
32. Wang J, Liu X, Dong D, et al. Prediction of malignant and benign of lung tumor using a quantitative radiomic method. Annu Int Conf IEEE Eng Med Biol Soc, 2016, 2016: 1272-1275.
33. 余烨, 吴华伟. 影像组学在肺癌中的应用进展. 国际医学放射学杂志, 2018, 41(6): 646-649.
34. 陈震东. 基于影像组学的肺肿瘤良恶性分类及早期肺腺癌淋巴结转移预测模型研究. 浙江师范大学, 2018.
35. 周天绮, 朱超挺, 石峰. 影像组学在肺肿瘤良恶性分类预测中的应用研究. 中国医疗器械杂志, 2020, 44(2): 113-117.
36. 黄志成, 叶钉利, 胡乔治, 郑君, 赵瑞坤. 基于CT影像组学模型鉴别诊断小细胞肺癌与非小细胞肺癌. 中国介入影像与治疗学, 2021, 18(8): 474-478.
37. 石镇维, 刘再毅. 影像组学研究的困境和出路. 中华放射学杂志, 2022, 56(1): 9-11.
38. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
39. 李雪, 周金治, 莫春梅, 等. 基于特征融合的U-Net肺自动分割方法. 中国医学物理学杂志, 2021, 38(6): 704-712.
40. 王生生, 王琪. 融合LBP和小波矩特征的肺癌图像精细分类. 东北师大学报(自然科学版), 2017, 49(2): 57-63.
41. Luo Z, Brubaker M A, Brudno M. Size and texture-based classification of lung tumors with 3D CNNs. Applications of Computer Vision. IEEE, 2017.
42. 郑德重. 基于多模态数据融合的肺部肿瘤智能分析技术研究. 中国科学院大学(中国科学院上海技术物理研究所), 2021.
43. Li L, Lu W, Tan S. Variational PET/CT tumor co-segmentation integrated with PET restoration. IEEE Trans Radiat Plasma Med Sci, 2020, 4(1): 37-49.
44. 张飞飞, 周涛, 陆惠玲, 等. 基于集成VPRS-RUGGA支持向量机的多模态肺部肿瘤计算机辅助诊断模型. 生物医学工程研究, 2019, 38(1): 48-53.
45. 吴翠颖, 周涛, 陆惠玲, 等. 基于集成SVM的肺部肿瘤PET/CT三模态计算机辅助诊断方法. 生物医学工程研究, 2017, 36(3): 207-212.
46. 梁蒙蒙. 基于卷积神经网络的多模态医学图像分类研究. 宁夏医科大学, 2019.
47. 武志远, 马圆, 唐浩, 等. 基于深度卷积神经网络方法构建肺部多模态图像分类诊断模型. 中国卫生统计, 2019, 36(6): 806-808, 813.
48. 王蕊芳. 基于卷积神经网络的多模态医学图像融合方法研究. 中北大学, 2021.
49. 王媛媛, 周涛, 陆惠玲, 等. 基于集成卷积神经网络的肺部肿瘤计算机辅助诊断模型. 生物医学工程学杂志, 2017, 34(4): 543-551.
50. 邬雪涛, 林岚, 王婧璇. 基于CT图像的肺实质分割技术研究进展. 智慧健康, 2019, 5(20): 87-89.
51. 石邈, 续力云, 潘鑫福, 等. 三维重建技术在肺腺癌新分类标准诊断中的价值. 中国胸心血管外科临床杂志, 2021, 28(3): 278-282.
52. 孙翎马. 肺部CT影像智能分析及辅助诊断关键技术研究. 电子科技大学, 2021.
53. 张紫程. 基于肺癌PET/CT影像的诊断模型研究. 兰州大学, 2019.
54. 任海玲, 周涛, 霍兵强. 基于集成DE-NRS的肺部肿瘤影像组学计算机辅助诊断模型. 计算机应用与软件, 2020, 37(5): 156-163, 204.
55. Hussein S, Kandel P, Bolan CW, et al. Lung and pancreatic tumor characterization in the deep learning era: Novel supervised and unsupervised learning approaches. IEEE Trans Med Imaging, 2019, 38(8): 1777-1787.
56. Naik A, Edla DR. Lung tumor classification using CNN- and GLCM-based features. 2021.
57. 霍兵强, 周涛, 陆惠玲, 等. 基于NRC和多模态残差神经网络的肺部肿瘤良恶性分类. 山东大学学报(工学版), 2020, 50(6): 59-67, 75.
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Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Research progress of auxiliary diagnosis classification algorithm for lung tumor imaging

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