Objective: To comprehensively analyze the CT features and radiomics features of silent pheochromocytoma, establish models to explore the value of radiomics in differential diagnosis. Methods: A retrospective analysis of 97 cases of pheochromocytoma confirmed by surgery and pathology was performed. They were divided into silent group and non-silent group according to the presence or absence of hypertension and the triad of headache, palpitation and hyperhidrosis. The CT image features and radiomics features of the patients were compared and analyzed. Least absolute shrinkage and selection operator(LASSO) analysis was conducted to select the optimized feature subset. The conventional CT features, radiomics features, radiomics features combined with conventional CT features models were constructed using logistic regression analysis. Receiver operating characteristic(ROC) curve was used to analyze the diagnostic value of the model. Results: There was no significant difference in gender and age between the two groups(P>0.05). Among the conventional CT features, there were significant differences between the two groups in whether the longest diameter was greater than 5 cm, whether the shape was regular, and the range of cystic degeneration(P<0.05). There were nosignificant difference between the two groups of lesions in location, presence or absence of calcification, hemorrhage and cystic shape, the average CT attenuation of arterial phase and venous phase, homogenous enhancement or not, and enhancement mode and degree(P>0.05). The area under curve(AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the radiomics features combined with conventional CT features model were 0.922(95%CI:0.870-0.974)、0.854、0.784、0.894、0.806、0.881 respectively, which were higher than the conventional CT feature model andradiomics features model. Conclusion: Although silent adrenal pheochromocytoma has no typical clinical symptoms, the CT findings have certain characteristics, and the radiomics features combined with conventional CT features model can significantly improve the differential efficacy. |
[1] NEUMANN H P H,YOUNG W F,ENG C.Pheochromocytoma and paraganglioma[J].N Engl J Me,2019,381(6):552-565.
[2] 中华医学会内分泌学分会肾上腺学组.嗜铬细胞瘤和副神经节瘤诊断治疗专家共识(2020版)[J].中华内分泌代谢杂志,2020,36(9):737-750.
[3] FARRUGIA F A,MARTIKOS G,TZANETIS P,et al.Pheochromocytoma,diagnosis and treatment:review of the literature[J].Endocr Regul,2017,51(3):168-181.
[4] GURIJT S,MACKSON N,SOMNATH G,et al.Clinically silent giant pheochromocytoma:a case report[J].J Evol Med Dent Sci,2015,4(14):2422-2427.
[5] CLIFTON-BLIGH R.Diagnosis of silent pheochromocytoma and paraganglioma[J].Expert Rev Endocrinol Metab,2013,8(1):47-57.
[6] GUPTA A,BAINS L,AGARWAL M K,et al.Giant cystic pheochromocytoma:a silent entity[J].Urol Ann,2016,8(3):384-386.
[7] 杨华,吴红花,布楠,等.寂静性嗜铬细胞瘤临床特点分析[J].中华医学杂志,2018,98(34):2727-2731.
[8] LAMBIN P,RIOS-VELAZQUEZ E,LEIJENAAR R,et al.Radiomics:Extracting more information from medical images using advanced feature analysis[J].Eur J Cancer,2012,48(4):441-446.
[9] 王从军,乔英.肾上腺无功能嗜铬细胞瘤的MSCT诊断[J].中国医药指南,2013,11(17):230-232.
[10] 井汉国.肾上腺隐匿性嗜铬细胞瘤诊断与治疗(附15例)[J].中华内分泌外科杂志,2010,4(1):46-47.
[11] 于万钧,李肇宏,周泽旺,等.隐匿功能性嗜铬细胞瘤的CT及MRI诊断[J].中国中西医结合影像学杂志,2016,14(1):60-62.
[12] LU Y,LI P,GAN W,et al.Clinical and pathological characteristics of hypertensive and normotensive adrenal pheochromocytomas[J].Exp Clin Endocrinol Diabetes,2016,124(6):372-379.
[13] 姜元军,孙志熙,宫大鑫,等.静止型嗜铬细胞瘤和非静止型嗜铬细胞瘤的比较[J].临床泌尿外科杂志,2003,18(4):212-213.
[14] CROUT J R,SJOERDSMA A.Turnover and metabolism of catecholamines in patients with pheochromocytoma[J].J Clin Invest,1964,43(1):94-102.
[15] LUBNER M G,SMITH A D,SANDRASEGARAN K,et al.CT texture analysis:definitions,applications,biologic correlates,and challenges[J].Radiographics,2017,37(5):1483-1503.
[16] 彭晓容,马春浓,陈恩炎.增强CT直方图分析与肺癌组织分化程度的相关性研究[J].医学影像学杂志,2017,27(9):1698-1700.
[17] 徐明哲,刘爱连,陈安良,等.平扫最佳单能量CT值直方图分析对肾乏脂性错构瘤与透明细胞癌的鉴别价值[J].放射学实践,2018,33(11):1173-1177.
[18] 贺江琳,王远军.阿尔茨海默病影像组学关键方法研究进展[J].中国医学影像技术,2019,35(10):1569-1573.
[19] THIBAULT G,FERTIL B,NAVARRO C,et al.Texture indexes and gray level size zone matrix application to cell nuclei classification[C]//10th International Conference on Pattern Recognition and Information Processing,Minsk,Belarus,2009:140-145. |