Radiomics model to classify mammary masses using breast DCE-MRI compared to the BI-RADS classification performance

被引：0
作者：
Kawtar Debbi
Paul Habert
Anaïs Grob
Anderson Loundou
Pascale Siles
Axel Bartoli
Alexis Jacquier
机构：
[1] La Timone Hôpital,Service de Radiologie
[2] Hôpital Nord,Service de Radiologie
[3] Aix Marseille Université,LIIE
[4] Aix Marseille Université,CERIMED
[5] Aix-Marseille Université,CEReSS UR3279
[6] Assistance Publique - Hôpitaux de Marseille,Health Service Research and Quality of Life Center
[7] Aix-Marseille Université,Department of Public Health
来源：
Insights into Imaging | / 14卷
关键词：
Breast neoplasms; Magnetic resonance imaging; Radiomics; Artificial intelligence;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The semi-automated breast tumor segmentation method allows extraction of radiomic features.A radiomics signature could be extracted from breast DCE-MRI and reach an AUC of 0.94 95%CI [0.85–1.00] on a test-set.There was no significant difference between the AUC ROC curves for the model (0.94) or the BI-RADS MRI (0.84) score (p = 0.19).
引用
收藏
相关论文
共 169 条
[1]  
Sung H(2021)Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries CA Cancer J Clin 71 209-249
[2]  
Ferlay J(2016)Diagnostic performance of breast magnetic resonance imaging in non-calcified equivocal breast findings: results from a systematic review and meta-analysis PLoS One 11 99-100
[3]  
Siegel RL(2019)Breast MRI: state of the art Radiology 292 520-536
[4]  
Laversanne M(2017)The potential of multiparametric MRI of the breast Br J Radiol 90 1-17
[5]  
Soerjomataram I(2015)Breast MRI: EUSOBI recommendations for women’s information Eur Radiol 25 3669-3678
[6]  
Jemal A(2015)Interobserver variability between breast imagers using the fifth edition of the BI-RADS MRI lexicon AJR Am J Roentgenol 204 1120-1124
[7]  
Bennani-Baiti B(2016)Radiomics: Images are more than pictures, they are data Radiology 278 563-577
[8]  
Bennani-Baiti N(2018)Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy Radiology 288 26-35
[9]  
Baltzer PA(2017)Breast cancer heterogeneity: MR Imaging texture analysis and survival outcomes Radiology 282 665-675
[10]  
Mann RM(2016)Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy J Magn Reson Imaging 44 1099-1106
12345678910