Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk
Terminal duct lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more likely to develop breast cancer. A major bottleneck in studying TDLU involution in large cohort studies is the need for labor-intensive manual assessmen...
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| creator | Wetstein, Suzanne C Onken, Allison M Luffman, Christina Baker, Gabrielle M Pyle, Michael E Kensler, Kevin H Liu, Ying Bakker, Bart Vlutters, Ruud van Leeuwen, Marinus B Collins, Laura C Schnitt, Stuart J Pluim, Josien P W Tamimi, Rulla M Heng, Yujing J Veta, Mitko |
| description | Terminal duct lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more likely to develop breast cancer. A major bottleneck in studying TDLU involution in large cohort studies is the need for labor-intensive manual assessment of TDLUs. We developed a computational pathology solution to automatically capture TDLU involution measures. Whole slide images (WSIs) of benign breast biopsies were obtained from the Nurses' Health Study. A set of 92 WSIs was annotated for acini, TDLUs and adipose tissue to train deep convolutional neural network (CNN) models for detection of acini, and segmentation of TDLUs and adipose tissue. These networks were integrated into a single computational method to capture TDLU involution measures including number of TDLUs per tissue area, median TDLU span and median number of acini per TDLU. We validated our method on 40 additional WSIs by comparing with manually acquired measures. Our CNN models detected acini with an F1 score of 0.73±0.07, and segmented TDLUs and adipose tissue with Dice scores of 0.84±0.13 and 0.87±0.04, respectively. The inter-observer ICC scores for manual assessments on 40 WSIs of number of TDLUs per tissue area, median TDLU span, and median acini count per TDLU were 0.71, 0.81 and 0.73, respectively. Intra-observer reliability was evaluated on 10/40 WSIs with ICC scores of >0.8. Inter-observer ICC scores between automated results and the mean of the two observers were: 0.80 for number of TDLUs per tissue area, 0.57 for median TDLU span, and 0.80 for median acini count per TDLU. TDLU involution measures evaluated by manual and automated assessment were inversely associated with age and menopausal status. We developed a computational pathology method to measure TDLU involution. This technology eliminates the labor-intensiveness and subjectivity of manual TDLU assessment, and can be applied to future breast cancer risk studies. |
| doi_str_mv | 10.1371/journal.pone.0231653 |
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Women with less TDLU involution are more likely to develop breast cancer. A major bottleneck in studying TDLU involution in large cohort studies is the need for labor-intensive manual assessment of TDLUs. We developed a computational pathology solution to automatically capture TDLU involution measures. Whole slide images (WSIs) of benign breast biopsies were obtained from the Nurses' Health Study. A set of 92 WSIs was annotated for acini, TDLUs and adipose tissue to train deep convolutional neural network (CNN) models for detection of acini, and segmentation of TDLUs and adipose tissue. These networks were integrated into a single computational method to capture TDLU involution measures including number of TDLUs per tissue area, median TDLU span and median number of acini per TDLU. We validated our method on 40 additional WSIs by comparing with manually acquired measures. Our CNN models detected acini with an F1 score of 0.73±0.07, and segmented TDLUs and adipose tissue with Dice scores of 0.84±0.13 and 0.87±0.04, respectively. The inter-observer ICC scores for manual assessments on 40 WSIs of number of TDLUs per tissue area, median TDLU span, and median acini count per TDLU were 0.71, 0.81 and 0.73, respectively. Intra-observer reliability was evaluated on 10/40 WSIs with ICC scores of >0.8. Inter-observer ICC scores between automated results and the mean of the two observers were: 0.80 for number of TDLUs per tissue area, 0.57 for median TDLU span, and 0.80 for median acini count per TDLU. TDLU involution measures evaluated by manual and automated assessment were inversely associated with age and menopausal status. We developed a computational pathology method to measure TDLU involution. This technology eliminates the labor-intensiveness and subjectivity of manual TDLU assessment, and can be applied to future breast cancer risk studies.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0231653</identifier><identifier>PMID: 32294107</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adipose tissue ; Adult ; Age Factors ; Artificial neural networks ; Biology and Life Sciences ; Biomedical engineering ; Biopsy ; Breast - pathology ; Breast cancer ; Breast Neoplasms - diagnosis ; Breast Neoplasms - epidemiology ; Breast Neoplasms - prevention & control ; Cohort Studies ; Computer and Information Sciences ; Computer applications ; Deep Learning ; Evaluation ; Female ; Health risks ; Humans ; Image Processing, Computer-Assisted ; Image segmentation ; Labor ; Machine learning ; Medical schools ; Medicine and Health Sciences ; Menopause ; Methods ; Middle Aged ; Milk ; Neural networks ; Pathology ; People and Places ; Reliability analysis ; Reproducibility of Results ; Research and Analysis Methods ; Risk Assessment ; Risk Factors ; Womens health</subject><ispartof>PloS one, 2020-04, Vol.15 (4), p.e0231653-e0231653</ispartof><rights>2020 Wetstein et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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This technology eliminates the labor-intensiveness and subjectivity of manual TDLU assessment, and can be applied to future breast cancer risk studies.</description><subject>Adipose tissue</subject><subject>Adult</subject><subject>Age Factors</subject><subject>Artificial neural networks</subject><subject>Biology and Life Sciences</subject><subject>Biomedical engineering</subject><subject>Biopsy</subject><subject>Breast - pathology</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - diagnosis</subject><subject>Breast Neoplasms - epidemiology</subject><subject>Breast Neoplasms - prevention & control</subject><subject>Cohort Studies</subject><subject>Computer and Information Sciences</subject><subject>Computer applications</subject><subject>Deep Learning</subject><subject>Evaluation</subject><subject>Female</subject><subject>Health risks</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Image 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Ulas</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-04-15</date><risdate>2020</risdate><volume>15</volume><issue>4</issue><spage>e0231653</spage><epage>e0231653</epage><pages>e0231653-e0231653</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Terminal duct lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more likely to develop breast cancer. A major bottleneck in studying TDLU involution in large cohort studies is the need for labor-intensive manual assessment of TDLUs. We developed a computational pathology solution to automatically capture TDLU involution measures. Whole slide images (WSIs) of benign breast biopsies were obtained from the Nurses' Health Study. A set of 92 WSIs was annotated for acini, TDLUs and adipose tissue to train deep convolutional neural network (CNN) models for detection of acini, and segmentation of TDLUs and adipose tissue. These networks were integrated into a single computational method to capture TDLU involution measures including number of TDLUs per tissue area, median TDLU span and median number of acini per TDLU. We validated our method on 40 additional WSIs by comparing with manually acquired measures. Our CNN models detected acini with an F1 score of 0.73±0.07, and segmented TDLUs and adipose tissue with Dice scores of 0.84±0.13 and 0.87±0.04, respectively. The inter-observer ICC scores for manual assessments on 40 WSIs of number of TDLUs per tissue area, median TDLU span, and median acini count per TDLU were 0.71, 0.81 and 0.73, respectively. Intra-observer reliability was evaluated on 10/40 WSIs with ICC scores of >0.8. Inter-observer ICC scores between automated results and the mean of the two observers were: 0.80 for number of TDLUs per tissue area, 0.57 for median TDLU span, and 0.80 for median acini count per TDLU. TDLU involution measures evaluated by manual and automated assessment were inversely associated with age and menopausal status. We developed a computational pathology method to measure TDLU involution. This technology eliminates the labor-intensiveness and subjectivity of manual TDLU assessment, and can be applied to future breast cancer risk studies.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32294107</pmid><doi>10.1371/journal.pone.0231653</doi><orcidid>https://orcid.org/0000-0002-8930-817X</orcidid><orcidid>https://orcid.org/0000-0001-8699-5739</orcidid><orcidid>https://orcid.org/0000-0001-7515-7270</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2020-04, Vol.15 (4), p.e0231653-e0231653 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2390143818 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adipose tissue Adult Age Factors Artificial neural networks Biology and Life Sciences Biomedical engineering Biopsy Breast - pathology Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - epidemiology Breast Neoplasms - prevention & control Cohort Studies Computer and Information Sciences Computer applications Deep Learning Evaluation Female Health risks Humans Image Processing, Computer-Assisted Image segmentation Labor Machine learning Medical schools Medicine and Health Sciences Menopause Methods Middle Aged Milk Neural networks Pathology People and Places Reliability analysis Reproducibility of Results Research and Analysis Methods Risk Assessment Risk Factors Womens health |
| title | Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk |
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