影像组学在肺结核中的研究进展
Advances in Imaging Histology in Pulmonary Tuberculosis
摘要: 结核病是由结核分枝杆菌复合体引起的一种感染性疾病。我国人口基数大,目前结核病仍然是危害我国公民健康的重要疾病。因此,结核病的早诊断、早治疗显得尤为重要。影像组学这一概念在2012年首次被提出,计算辅助下对图像深度特征进行提取,它可以帮助医生分析图像,提高诊断的准确性。本文就影像组学在结核中的应用作以下综述。
Abstract: Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis complex. With a large population base in China, tuberculosis is still an important disease that endangers the health of our citizens. Therefore, early diagnosis and treatment of tuberculosis is particularly important. The concept of imaging histology, which was first introduced in 2012, computationally assists in the ex-traction of image depth features, and it can help doctors to analyze images and improve the accura-cy of diagnosis. This paper provides the following review of the use of imaging histology in tubercu-losis.
文章引用:谭华清, 鲍海华, 曹云太, 夏弘婧. 影像组学在肺结核中的研究进展[J]. 临床医学进展, 2023, 13(5): 7624-7629. https://doi.org/10.12677/ACM.2023.1351065

参考文献

[1] Alashavi, H., Daher, M., Chorgoliani, D., et al. (2021) Descriptive Epidemiology of the Tuberculosis Service Delivery Project Beneficiaries in Northwest Syria: 2019-2020. Frontiers in Public Health, 9, Article ID: 672114. [Google Scholar] [CrossRef] [PubMed]
[2] 谢小义, 赵雪漪. 省域肺结核发病率的空间溢出效应研究[J]. 汉江师范学院学报, 2021, 41(3): 28-32. [Google Scholar] [CrossRef
[3] 阿旺央金, 索朗多布杰, 格桑尼玛, 郑武. 2014-2019年西藏山南市肺结核病流行病学分析[J/OL]. 公共卫生与预防医学: 1-3. http://kns.cnki.net/kcms/detail/42.1734.r.20210330.1652.004.html, 2022-12-02.
[4] 黎惠如, 方伟军, 刘曾维, 谢智恩. CT在单发结节或肿块型肺结核和肺癌鉴别中的作用研究[J]. 新发传染病电子杂志, 2021, 6(4): 323-326. [Google Scholar] [CrossRef
[5] 蔡久媺. 基于CT影像组学的计算机辅助鉴别周围型肺癌与肿块/结节型肺结核的研究[D]: [硕士学位论文]. 大连: 大连医科大学, 2020.[CrossRef
[6] Lambin, P., Rios-Velazquez, E., Leijenaar, R., et al. (2012) Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis. European Journal of Cancer, 48, 441-446. [Google Scholar] [CrossRef] [PubMed]
[7] Bortolotto, C., Lancia, A., Stelitano, C., et al. (2021) Radiomics Features as Predictive and Prognostic Biomarkers in NSCLC. Expert Review of Anticancer Therapy, 21, 257-266. [Google Scholar] [CrossRef] [PubMed]
[8] Avanzo, M., Stancanello, J. and El Naqa, I. (2017) Beyond Imaging: The Promise of Radiomics. Physica Medica, 38, 122-139. [Google Scholar] [CrossRef] [PubMed]
[9] Hassani, C., Varghese, B.A., Nieva, J., et al. (2019) Radiomics in Pulmonary Lesion Imaging. American Journal of Roentgenology, 212, 497-504. [Google Scholar] [CrossRef
[10] Gillies, R.J., Kinahan, P.E. and Hricak, H. (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology, 278, 563-577. [Google Scholar] [CrossRef] [PubMed]
[11] Bezzi, C., Mapelli, P., Presotto, L., et al. (2021) Radiomics in Pan-creatic Neuroendocrine Tumors: Methodological Issues and Clinical Significance. European Journal of Nuclear Medicine and Molecular Imaging, 48, 4002-4015. [Google Scholar] [CrossRef] [PubMed]
[12] Mayerhoefer, M.E., Materka, A., Langs, G., et al. (2020) Intro-duction to Radiomics. Journal of Nuclear Medicine, 61, 488-495. [Google Scholar] [CrossRef] [PubMed]
[13] Beig, N., Bera, K. and Tiwari, P. (2021) Introduction to Radi-omics and Radiogenomics in Neuro-Oncology: Implications and Challenges. Neuro-Oncology Advances, 2, iv3-iv14. [Google Scholar] [CrossRef] [PubMed]
[14] 李大龙, 何旦旦, 田燕, 曹新山. MSCT在结节或肿块型肺结核中的诊断价值[J]. 医学影像学杂志, 2020, 30(2): 204-207.
[15] 顾广红, 缪锦林, 张恒. 48例误诊为肺癌的肿块型肺结核CT表现回顾性分析[J]. 影像研究与医学应用, 2018, 2(18): 59-60.
[16] Zhao, W., Xiong, Z., Jiang, Y., et al. (2022) Radiomics Based on Enhanced CT for Differentiating between Pulmonary Tuberculosis and Pulmonary Adeno-carcinoma Presenting as Solid Nodules or Masses. Journal of Cancer Research and Clinical Oncology. [Google Scholar] [CrossRef] [PubMed]
[17] Feng, B., Chen, X., Chen, Y., et al. (2020) Radiomics Nomo-gram for Preoperative Differentiation of Lung Tuberculoma from Adenocarcinoma in Solitary Pulmonary Solid Nodule. European Journal of Radiology, 128, Article ID: 109022. [Google Scholar] [CrossRef] [PubMed]
[18] Cui, E.N., Yu, T., Shang, S.J., et al. (2020) Radiomics Model for Distinguishing Tuberculosis and Lung Cancer on Computed Tomography Scans. World Journal of Clinical Cases, 8, 5203-5212. [Google Scholar] [CrossRef] [PubMed]
[19] 程明远. 基于薄层CT影像组学模型对于空洞型肺癌与空洞型肺结核的相关性研究[D]: [硕士学位论文]. 南昌: 南昌大学, 2022.[CrossRef
[20] Du, D., Gu, J., Chen, X., et al. (2021) Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer. Molecular Imaging and Biology, 23, 287-298. [Google Scholar] [CrossRef] [PubMed]
[21] 樊梦思, 赵红, 曹捍波, 余业洲, 邹立巍, 段绍峰. 基于CT平扫影像组学模型鉴别结节/肿块型肺隐球菌病及肺腺癌与肺结核[J]. 中国医学影像技术, 2020, 36(6): 853-857. [Google Scholar] [CrossRef
[22] Zhao, W., Xiong, Z., Tian, D., et al. (2022) The Add-ing Value of Contrast-Enhanced CT Radiomics: Differentiating Tuberculosis from Non-Tuberculous Infectious Lesions Presenting as Solid Pulmonary Nodules or Masses. Frontiers in Public Health, 10, Article ID: 1018527. [Google Scholar] [CrossRef] [PubMed]
[23] Yan, Q., Wang, W., Zhao, W., et al. (2022) Differentiating Non-tuberculous Mycobacterium Pulmonary Disease from Pulmonary Tuberculosis through the Analysis of the Cavity Fea-tures in CT Images Using Radiomics. BMC Pulmonary Medicine, 22, Article No. 4. [Google Scholar] [CrossRef] [PubMed]
[24] 朱晓夏, 陈言语, 周晓俊, 鲍方进. 结核病患者结核分枝杆菌耐药性检测结果分析[J]. 热带医学杂志, 2022, 22(7): 945-947.
[25] 吴燕飞, 徐丽霞, 赖聪娟, 季柏林, 周琛博, 许河南. 荧光PCR熔解曲线法对痰样本结核分枝杆菌耐药性检测价值及耐药特征分析[J]. 浙江中西医结合杂志, 2022, 32(10): 949-952.
[26] 李倩, 邓彬彬, 高朋健, 周杰斌, 侯周华. mNGS在结核性胸膜炎诊断和耐药性评估中应用价值[J]. 湖南师范大学学报(医学版), 2021, 18(5): 87-92.
[27] 徐晓娜, 宋衍燕, 肖迪. 结核分枝杆菌耐药性检测技术研究进展[J]. 中国预防医学杂志, 2023, 24(3): 274-280. [Google Scholar] [CrossRef
[28] Gröschel, M.I., Owens, M., Freschi, L., et al. (2021) GenTB: A User-Friendly Genome-Based Predictor for Tuberculosis Resistance Powered by Machine Learning. Genome Medicine, 13, Article No. 138. [Google Scholar] [CrossRef] [PubMed]
[29] Jaeger, S., Juarez-Espinosa, O.H., Candemir, S., et al. (2018) Detecting Drug-Resistant Tuberculosis in Chest Radiographs. The International Journal for Computer Assisted Radiol-ogy and Surgery, 13, 1915-1925. [Google Scholar] [CrossRef] [PubMed]
[30] Li, Y., Wang, B., Wen, L., et al. (2022) Machine Learning and Radiomics for the Prediction of Multidrug Resistance in Cavitary Pulmonary Tuberculosis: A Multicentre Study. Euro-pean Radiology, 33, 391-400. [Google Scholar] [CrossRef] [PubMed]
[31] Gao, X.W. and Qian, Y. (2018) Prediction of Multi-drug-Resistant TB from CT Pulmonary Images Based on Deep Learning Techniques. Molecular Pharmaceutics, 15, 4326-4335. [Google Scholar] [CrossRef] [PubMed]