Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning.
Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning.
复制标题
使用深度学习自动量化乳腺终末导管小叶（TDLU）退化水平。
DOI：
10.1038/s41523-021-00378-7
复制
发表时间：
2022-01-19
影响因子：
5.9
通讯作者：
van der Laak JAWM
中科院分区：
文献类型：
--
作者：
de Bel T;Litjens G;Ogony J;Stallings-Mann M;Carter JM;Hilton T;Radisky DC;Vierkant RA;Broderick B;Hoskin TL;Winham SJ;Frost MH;Visscher DW;Allers T;Degnim AC;Sherman ME;van der Laak JAWM
Convolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the vast majority of tissue features, including involution of background terminal duct lobular units (TDLUs), the structures from which breast cancers arise. Studies indicate that increased levels of age-related TDLU involution in BBD biopsies predict lower breast cancer risk, and therefore its assessment may have potential value in risk assessment and management. However, assessment of TDLU involution is time-consuming and difficult to standardize and quantitate. Accordingly, we developed a CNN to enable automated quantitative measurement of TDLU involution and tested its performance in 174 specimens selected from the pathology archives at Mayo Clinic, Rochester, MN. The CNN was trained and tested on a subset of 33 biopsies, delineating important tissue types. Nine quantitative features were extracted from delineated TDLU regions. Our CNN reached an overall dice-score of 0.871 (±0.049) for tissue classes versus reference standard annotation. Consensus of four reviewers scoring 705 images for TDLU involution demonstrated substantial agreement with the CNN method (unweighted κappa = 0.747 ± 0.01). Quantitative involution measures showed anticipated associations with BBD histology, breast cancer risk, breast density, menopausal status, and breast cancer risk prediction scores (p < 0.05). Our work demonstrates the potential to improve risk prediction for women with BBD biopsies by applying CNN approaches to generate automated quantitative evaluation of TDLU involution.
登录
查看更多内容
影响因子：
3.8
作者：
Figueroa, Jonine D.;Pfeiffer, Ruth M.;Brinton, Louise A.;Palakal, Maya M.;Degnim, Amy C.;Radisky, Derek;Hartmann, Lynn C.;Frost, Marlene H.;Mann, Melody L. Stallings;Papathomas, Daphne;Gierach, Gretchen L.;Hewitt, Stephen M.;Duggan, Maire A.;Visscher, Daniel;Sherman, Mark E.
通讯作者：
Sherman, Mark E.
影响因子：
6.2
作者：
HENSON, DE;TARONE, RE
通讯作者：
TARONE, RE
影响因子：
11.5
作者：
Degnim, Amy C.;Hoskin, Tanya L.;Visscher, Daniel W.
通讯作者：
Visscher, Daniel W.
影响因子：
6.2
作者：
Baer HJ;Collins LC;Connolly JL;Colditz GA;Schnitt SJ;Tamimi RM
通讯作者：
Tamimi RM
影响因子：
3.8
作者：
Radisky DC;Visscher DW;Frank RD;Vierkant RA;Winham S;Stallings-Mann M;Hoskin TL;Nassar A;Vachon CM;Denison LA;Hartmann LC;Frost MH;Degnim AC
通讯作者：
Degnim AC