[1]李如冰,彭 梅,毕 玉,等.人工智能S-Detect技术联合BI-RADS分类及Adler分级法在乳腺肿块诊断中的价值[J].医学信息,2022,35(20):32-36.[doi:10.3969/j.issn.1006-1959.2022.20.007]
　LI Ru-bing,PENG Mei,BI Yu,et al.Value of Artificial Intelligence S-Detect Technique Combined with BI-RADS Classification and Adler Classification in Diagnosis of Breast Masses[J].Journal of Medical Information,2022,35(20):32-36.[doi:10.3969/j.issn.1006-1959.2022.20.007]
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人工智能S-Detect技术联合BI-RADS分类及Adler分级法在乳腺肿块诊断中的价值()

医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
35卷
期数:
2022年20期
页码:
32-36
栏目:
论著
出版日期:
2022-10-15

文章信息/Info

Title:
Value of Artificial Intelligence S-Detect Technique Combined with BI-RADS Classification and Adler Classification in Diagnosis of Breast Masses
文章编号:
1006-1959（2022）20-0032-05
作者:
李如冰; 彭 梅; 毕 玉; 等
（安徽医科大学第二附属医院超声诊断科，安徽 合肥 230601）
Author(s):
LI Ru-bing; PENG Mei; BI Yu; et al.
(Department of Ultrasound Diagnosis,the Second Hospital of Anhui Medical University,Hefei 230601,Anhui,China)
关键词:
S-Detect技术; 彩色多普勒血流显像; 乳腺影像报告数据系统; 乳腺肿块
Keywords:
S-Detect technology; Color Doppler flow imaging; Breast Imaging Reporting Data System; Breast lumps
分类号:
R445.1
DOI:
10.3969/j.issn.1006-1959.2022.20.007
文献标志码:
A
摘要:
目的 探讨人工智能S-Detect技术联合BI-RADS分类及Adler分级法诊断乳腺肿块中的价值。方法 收集2019年9月-2021年7月在安徽医科大学第二附属医院超声诊断科行超声检查的121例乳腺肿块患者的超声图像资料，分析BI-RADS分类、S-Detect及Adler分级法诊断结果，根据S-Detect及Adler分级法诊断结果将每个肿块的分类进行联合诊断，以病理结果为金标准，比较BI-RADS分类、BI-RADS联合S-Detect技术、BI-RADS联合S-Detect技术及Adler分级法诊断乳腺肿块的灵敏度、特异度、准确度，采用受试者操作曲线（ROC）分析三种诊断方法的价值。结果 病理结果显示，121例乳腺肿块患者恶性65例，良性56例。BI-RADS分类、BI-RADS联合S-Detect技术、BI-RADS联合S-Detect技术及Adler分级法的AUC分别为0.644、0.663、0.823；BI-RADS分类诊断乳腺肿块的灵敏度为98.46%、特异度为30.36%、准确度为66.94%；BI-RADS联合S-Detect诊断乳腺肿块的灵敏度为96.92%，特异度为35.71%，准确度为68.60%；BI-RADS联合S-Detect及Adler分级法诊断乳腺肿块的灵敏度为98.46%，特异度为66.07%，准确度为83.47%。结论 人工智能S-Detect技术联合BI-RADS分类及Adler分级法可进一步提升对乳腺肿块的诊断效能，对临床干预与治疗具有重要的指导意义。
Abstract:
Objective To explore the value of artificial intelligence S-Detect technology combined with BI-RADS classification and Adler classification in the diagnosis of breast masses.Methods The ultrasound image data of 121 patients with breast masses who underwent ultrasound examination in the Department of Ultrasound Diagnosis, the Second Hospital of Anhui Medical University from September 2019 to July 2021 were collected. The diagnostic results of BI-RADS classification, S-Detect and Adler classification were analyzed. According to the diagnostic results of S-Detect and Adler classification, the classification of each mass was jointly diagnosed, and the pathological results were used as the gold standard. The sensitivity, specificity and accuracy of BI-RADS classification, BI-RADS combined with S-Detect technology, BI-RADS combined with S-Detect technology and Adler grading method in the diagnosis of breast masses were compared. The value of the three diagnostic methods was analyzed by receiver operating curve (ROC).Results The pathological results showed that 65 cases were malignant and 56 cases were benign in 121 patients with breast masses. The AUC of BI-RADS classification, BI-RADS combined with S-Detect technology, BI-RADS combined with S-Detect technology and Adler grading method were 0.644, 0.663 and 0.823, respectively. The sensitivity, specificity and accuracy of BI-RADS classification in the diagnosis of breast masses were 98.46%, 30.36% and 66.94%, respectively. The sensitivity, specificity and accuracy of BI-RADS combined with S-Detect in the diagnosis of breast masses were 96.92%, 35.71% and 68.60%, respectively. The sensitivity, specificity and accuracy of BI-RADS combined with S-Detect and Adler grading in the diagnosis of breast masses were 98.46%, 66.07% and 83.47%, respectively.Conclusion Artificial intelligence S-Detect technology combined with BI-RADS classification and Adler classification can further improve the diagnostic efficiency of breast masses, and has important guiding significance for clinical intervention and treatment.

参考文献/References:

[1]侯怡如,张宇虹,于韬.青年乳腺癌与中老年乳腺癌超声影像学对比研究[J].大连医科大学学报,2016,38(6):574-578.[2]Jiang T,Jiang Y,Chen W,et al.Chinese association of ultrasound in medicine and engineering, superficial organs and peripheral vessels committee expert consensus on clinical frequently asked questions in breast ultrasonography,June 2018[J].J Cancer Res Ther,2018,14(7):1463-1468.[3]周永刚,袁丽君,邢长洋,等.超声S-Detect分类技术在乳腺包块良恶性诊断中的应用价值[J].中华超声影像学杂志,2017,26(12):1053-1056.[4]马洁玲,王心怡,张楠,等.S-Detect超声检查对乳腺病灶诊断价值的探索性研究[J].中华医学超声杂志（电子版）,2020,17(8):748-752.[5]李响,程慧芳,闫虹,等.常规超声联合S-Detect技术对乳腺病灶的诊断价值[J].中国超声医学杂志,2019,35(3):225-228.[6]葛艳艳,王觉晗,胡艳兵,等.常规超声与S-Detect技术联合诊断乳腺肿物良恶性的应用价值[J].中国实验诊断学,2020,24(12):1996-1997.[7]李响,程慧芳,闫虹,等.常规超声联合S-detect技术前后的诊断价值比较[C]//中国超声医学工程学会.中国超声医学工程学会第十二届全国腹部超声医学学术大会论文汇编.2018:1.[8]Adler DD,Carson PL,Rubin JM,et al.Doppler ultrasound color flow Imaging in the study ofbreast cancer:preliminaryfindings[J].Ultrasoundin Med Biol,1990,16(6):553-559.[9]Rebecca L.Siegel MPH and Kimberly D.Miller MPH and Ahmedin Jemal DVM, PhD.Cancer Statistics,2017[J].A CancerJournal for Clinicians,2017,67(1):7-30.[10]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[J].CA Cancer J Clin,2021,71(3):209-249.[11]陈彦博,胡婷婷,陈凯,等.乳腺癌患者保留乳房手术后复发、转移危险因素分析[J].中华乳腺病杂志(电子版),2021,15(3):143-151.[12]杨玉枝.女性乳腺癌流行病学调查及危险因素分析[J].黑龙江中医药,2020,49(4):182-183.[13]Wang J,Liu X,Dong D,et al.Prediction of malignant and be- nign of lung tumor using a quantitative radiomic method[J].Conf Proc IEEE Eng Med Biol Soc,2016,2016:1272-1275.[14]Li J,Sang T,Yu WH,et al.The value of S-Detect for the differential diagnosis of breast masses on ultrasound: a systematic review and pooled meta-analysis[J].Medical Ultrasonography,2020,22(2):211-219.[15]Choi JH,Kang BJ,Baek JE,et al.Application of computer-aided diagnosis in breast ultrasound interpretation:improvements in diagnostic performance according to reader experience[J].Ultrasonography,2017,37(3):217-225.[16]王心宇,魏琪,崔新伍,等.S-Detect技术在乳腺癌鉴别诊断中的辅助诊断价值[J].中华超声影像学杂志,2019(3):246-250.[17]Sun Q,Lin X,Zhao Y,et al.Deep learning vs.radiomics for predicting axillary lymph node metastasis of breast cancer using ultrasound images: don’t forget the peritumoral region[J].Front Oncol,2020,10:53.[18]李世玉,张群霞,王志刚.乳腺癌超声表现与生物学预后因子的相关性[J].中国医学影像学杂志,2017,25(12):950-952.[19]邢博缘,赵云,平杰,等.S-Detect(TM)分类技术诊断BI-RADS 4类乳腺肿块[J].中国医学影像技术,2020,36(9):1319-1323.[20]李亮,曹云云,牛建梅.乳腺导管内癌超声图像特征及病理相关性[J].肿瘤影像学,2020,29(5):442-447.

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更新日期/Last Update: 1900-01-01