ArticlePloS one2020
Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 47 citations in OpenAlex.
- Normal breast tissue (NBT)-classifiers: advancing compartment classification in normal breast histology.NPJ breast cancer · 2026Article
- Associations of Stem Cell Markers with Lobular Involution in Benign Breast Tissue.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Article
- Cellular senescence predicts breast cancer risk from benign breast disease biopsy images.Breast cancer research : BCR · 2025Article
- Relationship between breast tissue involution and breast cancer.Frontiers in oncology · 2025Review
- The Genomic and Biologic Landscapes of Breast Cancer and Racial Differences.International journal of molecular sciences · 2024Review
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Associations between quantitative measures of TDLU involution and breast tumor molecular subtypes among breast cancer cases in the Black Women's Health Study: a case-case analysis.Breast cancer research : BCR · 2022Article
- Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images.Scientific reports · 2022Article
- Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning.NPJ breast cancer · 2022Article
- TDLU Involution and Breast Cancer Risk-Reply.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021Article
- Deep learning-based grading of ductal carcinoma in situ in breast histopathology images.Laboratory investigation; a journal of technical methods and pathology · 2021Article
- Breast tumor-on-chip models: From disease modeling to personalized drug screening.Journal of controlled release : official journal of the Controlled Release Society · 2021Review
- Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer.JNCI cancer spectrum · 2021Article
- Automated Quantitative Measures of Terminal Duct Lobular Unit Involution and Breast Cancer Risk.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 6 institutions in 2 countries.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.