CT影像组学在胸部疾病诊疗中的应用新进展
New Progress in the Application of CT Imaging Omics in the Diagnosis and Treatment of Chest Diseases
DOI: 10.12677/ACM.2023.13122653, PDF,   
作者: 吐尔孙阿依·米吉提:新疆医科大学第一临床医学院,新疆 乌鲁木齐
关键词: 自发性气胸CT影像组学纹理分析Spontaneous Pneumothorax CT Imaging Histology Texture Analysis
摘要: 气胸(pneumothorax)系肺组织及脏层胸膜破裂或胸壁及壁层胸膜被穿透空气进入胸膜腔,形成胸膜腔积气和肺组织的压缩。在无创伤或人为情况下,肺组织及脏层胸膜自发破裂,空气进入胸膜腔,导致肺组织受压,引发一系列综合征称为自发性气胸(Spontaneous pneumothorax, SP)。自发性气胸又分为原发性自发性气胸和继发性自发性气胸。原发性自发性气胸(Primary spontaneous pneu-mothorax, PSP)指肺脏实质或脏层胸膜在无外源性或介入性因素影响以及无基础性肺疾病条件下,自行发生破裂,引起气体在胸膜腔蓄积,男性发病率18~28/10万,而女性发病率要低的多,大约为1.2~6.0/10万;继发性自发性气胸是指继发于肺脏各种疾病,如慢性肺结核、弥漫性肺间质纤维化、肺癌等。原发性自发性气胸的临床症状比较典型,多在休息时发病,症状多为伴或不伴呼吸困难的突发性胸痛。自发性气胸(SP)是临床上比较常见的呼吸系统疾病,呼吸困难、胸痛等是该疾病主要的临床表现,若不及时接受治疗,则可能会损害患者的肺功能,诱发皮下气肿、纵膈气肿、血气胸等并发症,给患者的日常生活质量和工作状态带来不便。严重时还会危及患者的生命,给患者的生命健康安全造成极大的威胁。
Abstract: Pneumothorax refers to the rupture of the lung tissue and visceral pleura, or the penetration of air into the pleural cavity of the chest wall and parietal pleura, resulting in pneumothorax and com-pression of lung tissue. Without trauma or artificial circumstances, the lung tissue and visceral pleura spontaneously rupture, air enters the pleural cavity, leading to compression of the lung tis-sue, which leads to a series of syndromes called spontaneous pneumothorax (SP). Spontaneous pneumothorax can be divided into primary spontaneous pneumothorax and secondary spontane-ous pneumothorax. Primary spontaneous pneumothorax (PSP) refers to the spontaneous rupture of the lung parenchyma or visceral pleura without the influence of exogenous or interventional factors and without basic lung diseases, resulting in the accumulation of gas in the pleural cavity. The inci-dence rate of men is 18~28/100,000, while that of women is much lower, about 1.2~6.0/100,000; Secondary spontaneous pneumothorax refers to various diseases secondary to the lungs, such as chronic pulmonary tuberculosis, diffuse pulmonary interstitial fibrosis, lung cancer, etc. The clinical symptoms of primary spontaneous pneumothorax are typical, which usually occur at rest. The symptoms are usually sudden chest pain with or without dyspnea. Spontaneous pneumothorax (SP) is a common disease in clinic. The main clinical manifestations of SP are dyspnea and chest pain, which bring inconvenience to the quality of daily life and working status of patients. If patients with spontaneous pneumothorax do not receive treatment in time, they may damage their lung function, induce subcutaneous emphysema, mediastinal emphysema, hemopneumothorax and other com-plications, and even endanger their lives when serious, posing a great threat to their health and safety.
文章引用:吐尔孙阿依·米吉提. CT影像组学在胸部疾病诊疗中的应用新进展[J]. 临床医学进展, 2023, 13(12): 18860-18863. https://doi.org/10.12677/ACM.2023.13122653

参考文献

[1] 唐彩银, 李瑗, 张继, 等. CT纹理分析在肾脏透明细胞癌分级的临床应用[J]. 医学理论与实践, 2019, 32(21): 3416-3418, 3409.
[2] Mir, A.H., hanmandlu, M. and Tandon, S.N. Texture Analysis of CT Images. IEEE Engineering in Medicine and Biology Magazine, 14, 781-786.[CrossRef
[3] Corrias, G., Micheletti, G., Barberini, L., Suri, J.S. and Saba, L. (2022) Texture Analysis Imaging “What a Clinical Radiologist Needs to Know”. European Journal of Radiology, 146, Article ID: 110055. [Google Scholar] [CrossRef] [PubMed]
[4] Tuceryan, M. and Jain, A.K. (1998) Texture Analysis. In: Chen, C.H. and Pau, L.F., Eds., Handbook of Pattern Recognition and Computer Vision, World Scientific Publishing Co., 207-248. [Google Scholar] [CrossRef
[5] Uppaluri, R., Mitsa, T., Sonka, M., Hoffman, E.A. and McLennan, G. (1997) Quantification of Pulmonary Emphysema from Lung Computed Tomography Images. Ameri-can Journal of Respiratory and Critical Care Medicine, 156, 248-254. [Google Scholar] [CrossRef] [PubMed]
[6] Reginelli, A., Belfiore, M.P., Monti, R., Cozzolino, I., Costa, M., Vicidomini, G., Grassi, R., Morgillo, F., Urraro, F., Nardone, V. and Cappabianca, S. (2020) The Texture Analysis as a Predictive Method in the Assessment of the Cytological Specimen of CT-Guided FNAC of the Lung Cancer. Medi-cal Oncology, 37, Article Number: 54. [Google Scholar] [CrossRef] [PubMed]
[7] Brunese, L., Mercaldo, F., Reginelli, A. and Santone, A. (2020) An Ensemble Learning Approach for Brain Cancer Detection Exploiting Radiomic Features. Computer Methods and Programs in Biomedicine, 185, Article ID: 105134. [Google Scholar] [CrossRef] [PubMed]
[8] Brunese, L., Mercaldo, F., Reginelli, A. and Santone, A. (2020) Formal Methods for Prostate Cancer Gleason Score and Treatment Prediction Using Radiomic Biomarkers. Magnetic Resonance Imaging, 66, 165-175. [Google Scholar] [CrossRef] [PubMed]
[9] Gillies, R.J., Kinahan, P.E. and Hricak, H. (2015) Radiomics: Im-ages Are More than Pictures, They Are Data. Radiology, 278, 563-577.
[10] Müller, N.L., Staples, C.A., Miller, R.R. and Abboud, R.T. (1988) Density Mask. An Objective Method to Quantitate Emphysema Using Computed Tomography. Chest, 94, 782-787. [Google Scholar] [CrossRef] [PubMed]
[11] Bankier, A.A., Maertelaer, V.D., Keyzer, C. and Gevenois, P.A. (1999) Pulmonary Emphysema: Subjective Visual Grading versus Objective Quantification with Macro-scopic Morphometry and Thin-Section CT Densitometry. Radiology, 211, 851-858. [Google Scholar] [CrossRef] [PubMed]
[12] Wibmer, A., Hricak, H., Gondo, T., et al. (2015) Haralick Texture Analysis of Prostate MRl: Utility for Differentiating Non-Cancerous Prostate from Prostate Cancer and Differen-tiating with Different Gleason Scores. European Radiology, 25, 2840-2850. [Google Scholar] [CrossRef] [PubMed]
[13] Fehr, D., Veeraraghavanm H., Wibmer, A., et al. (2015) Auto-matic Classification of Prostate Cancer Gleason Sores from Multi-Parametric Magnetic Resonance Images. Proceedings of the National Academy of Sciences of the United States of America, 112, E6265-E6273.
[14] Ohno, Y., Aoyagi, K., Takenaka, D., Yoshikawa, T., Ikezaki, A., Fujisawa, Y., Murayama, K., Hattori, H. and Toyama, H. (2021) Machine Learning for Lung CT Texture Analysis: Improvement of Inter-Observer Agreement for Radiological Finding Classifica-tion in Patients with Pulmonary Diseases. European Journal of Radiology, 134, Article ID: 109410. [Google Scholar] [CrossRef] [PubMed]
[15] Grove, O., Berglund, A.E., Schabath, M.B., Aerts, H.J.W.L., Dekker, A., et al. (2021) Correction: Quantitative Computed Tomographic Descriptors Associate Tumor Shape Com-plexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma. PLOS ONE, 16, e0248541. [Google Scholar] [CrossRef] [PubMed]
[16] Sørensen, L., Igel, C., Hansen, N.L., et al. (2016) Early Detec-tion of Alzheimer’s Disease Using MRI Hippocampal Texture. Human Brain Mapping, 37, 1148-1161. [Google Scholar] [CrossRef] [PubMed]
[17] 王朝, 邹卫. 原发性自发性气胸病因研究进展[J]. 临床肺科杂志, 2015, 20(6): 1120-1122, 1126.