Automated Quantitative Measures of Terminal Duct Lobular Unit Involution and Breast Cancer Risk

Kensler, Kevin H.; Liu, Emily Z.F.; Wetstein, Suzanne C.; Onken, Allison; Luffman, Christina; Baker, Gabrielle M.; Collins, Laura C.; Schnitt, Stuart J.; Bret-Mounet, Vanessa C.; Veta, Mitko; Pluim, Josien P. W.; Liu, Ying; Colditz, Graham A.; Eliassen, A. Heather; Hankinson, Susan E.; Tamimi, Rulla M.; Heng, Yujing J.

doi:10.1158/1055-9965.epi-20-0723

Cited by 11 publications

(11 citation statements)

References 28 publications

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“…Gertig et al ( 59 ) and Gabrielson et al ( 68 ) observed more epithelium and less stroma in parous women compared with nulliparous women. Our findings in multiparous women who had a live birth within the last 20 years were similar to other studies that observed less TDLU involution in parous vs nulliparous women ( 30 , 69 ); supported epidemiological reports of increased BC risk in parous women who had a live birth within the last 5 to 24 years compared with nulliparous women ( 70 ); and highlighted the extensive stroma remodeling in mammary glands during pregnancy to accommodate expanding epithelium ( 71 ). The correlation between age of menarche and proportion of stroma reported by us and others ( 59 , 68 ) is in line with a higher percent breast density in young women who had later ages of menarche ( 72 ).…”

Section: Discussionsupporting

confidence: 91%

“…The null associations between age of first birth and length of breastfeeding with breast tissue composition agreed with Gertig et al ( 59 ), whereas Gabrielson et al ( 68 ) found an association between percentage of epithelium and length of breastfeeding, but not percentage of stroma. Using our other method that measures normal TDLUs, we also did not find an association between length of breastfeeding and TDLU involution ( 30 ). Older women have less dense breasts than younger women, with the greatest change in density occurring during the menopause years ( 73 ).…”

Section: Discussionmentioning

confidence: 69%

“…Details about the study design methods for the NHS and NHSII have been published previously ( 34 ). Eligible women with biopsy-confirmed BBD were placed into 2 substudies—the BBD Incidence study ( 35‐38 ) and/or the BBD BC nested case-control study ( 2 , 3 , 5 , 24 , 30 , 32 , 33 , 39‐41 ). BC diagnosis was confirmed verbally by the participant, via medical record review, or via the cancer registry.…”

Section: Methodsmentioning

confidence: 99%

“…For example, terminal duct lobular unit (TDLU) involution assessed using qualitative and semi-quantitative methods was suggested to be linked to lower BC risk ( 24‐27 ). We developed and applied an automated deep-learning method to capture quantitative measures of TDLU involution ( 28 , 29 ) in a large, nested case-control study ( 30 ).…”

mentioning

confidence: 99%

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Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Vellal

Sirinukunwattan

Kensler

et al. 2021

JNCI Cancer Spectrum

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Background New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method to the BBD BC nested case-control study within the Nurses’ Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of BC. Methods Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 whole slide images from 293 cases who developed BC and 1132 controls who did not. Percentages of each tissue region were calculated, and 615 morphometric features were extracted. Elastic net regression was used to create a BC morphometric signature. Associations between BC risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and body mass index evaluated the relationship between tissue composition and BC risk. All statistical tests were 2-sided. Results Among controls, direction of associations between BBD subtypes, parity, and number of births with breast composition varied by tissue region; select regions were associated with childhood body size, body mass index, age of menarche, and menopausal status (all P < .05). A higher proportion of epithelial tissue was associated with increased BC risk (odds ratio = 1.39, 95% confidence interval = 0.91 to 2.14, for highest vs lowest quartiles, Ptrend = .047). No morphometric signature was associated with BC. Conclusions The amount of epithelial tissue may be incorporated into risk assessment models to improve BC risk prediction.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 69%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Vellal

Sirinukunwattan

Kensler

et al. 2021

JNCI Cancer Spectrum

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“…Our group recently developed a novel deep-learning based computational pathology method to capture quantitative measures of breast terminal duct lobular unit (TDLU) involution 22,23 , a purported biomarker of breast cancer. We reported that neither quantitative measures of TDLU involution derived by our technology nor traditional manual qualitative assessment by pathologists was associated with breast cancer among women diagnosed with BBD 24 .…”

Section: Introductionmentioning

confidence: 79%

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Vellal

Sirinukunwattana

Kensler

et al. 2020

Preprint

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Background New biomarkers of risk may improve breast cancer risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images (WSIs) into epithelium, fibrous stroma, and fat. We applied our method to the BBD breast cancer nested case-control study within the Nurses' Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of breast cancer. Methods Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 WSIs from 293 cases who developed breast cancer and 1132 controls who did not. Percentages of each tissue region were calculated and 615 morphometric features were extracted. Elastic net regression was used to create a breast cancer morphometric signature. Associations between breast cancer risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and BMI evaluated the relationship between tissue composition and breast cancer risk. Results Among controls, BBD subtypes, parity, and number of births were differentially associated with all three tissue regions (p<0.05); select regions were associated with childhood body size, BMI, age of menarche, and menopausal status (p<0.05). Higher proportion of epithelial tissue was associated with increased breast cancer risk (OR=1.39, 95% CI 0.91-2.14 comparing highest and lowest quartiles; p-trend<0.05). No morphometric signature was associated with breast cancer. Conclusion The amount of epithelial tissue may be incorporated into risk assessment models to improve breast cancer risk prediction.

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Benign Breast Disease and Breast Cancer Risk in the Percutaneous Biopsy Era

Sherman,

Vierkant,

Winham

et al. 2024

JAMA Surg

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ImportanceBenign breast disease (BBD) comprises approximately 75% of breast biopsy diagnoses. Surgical biopsy specimens diagnosed as nonproliferative (NP), proliferative disease without atypia (PDWA), or atypical hyperplasia (AH) are associated with increasing breast cancer (BC) risk; however, knowledge is limited on risk associated with percutaneously diagnosed BBD.ObjectivesTo estimate BC risk associated with BBD in the percutaneous biopsy era irrespective of surgical biopsy.Design, Setting, and ParticipantsIn this retrospective cohort study, BBD biopsy specimens collected from January 1, 2002, to December 31, 2013, from patients with BBD at Mayo Clinic in Rochester, Minnesota, were reviewed by 2 pathologists masked to outcomes. Women were followed up from 6 months after biopsy until censoring, BC diagnosis, or December 31, 2021.ExposureBenign breast disease classification and multiplicity by pathology panel review.Main OutcomesThe main outcome was diagnosis of BC overall and stratified as ductal carcinoma in situ (DCIS) or invasive BC. Risk for presence vs absence of BBD lesions was assessed by Cox proportional hazards regression. Risk in patients with BBD compared with female breast cancer incidence rates from the Iowa Surveillance, Epidemiology, and End Results (SEER) program were estimated.ResultsAmong 4819 female participants, median age was 51 years (IQR, 43-62 years). Median follow-up was 10.9 years (IQR, 7.7-14.2 years) for control individuals without BC vs 6.6 years (IQR, 3.7-10.1 years) for patients with BC. Risk was higher in the cohort with BBD than in SEER data: BC overall (standard incidence ratio [SIR], 1.95; 95% CI, 1.76-2.17), invasive BC (SIR, 1.56; 95% CI, 1.37-1.78), and DCIS (SIR, 3.10; 95% CI, 2.54-3.77). The SIRs increased with increasing BBD severity (1.42 [95% CI, 1.19-1.71] for NP, 2.19 [95% CI, 1.88-2.54] for PDWA, and 3.91 [95% CI, 2.97-5.14] for AH), comparable to surgical cohorts with BBD. Risk also increased with increasing lesion multiplicity (SIR: 2.40 [95% CI, 2.06-2.79] for ≥3 foci of NP, 3.72 [95% CI, 2.31-5.99] for ≥3 foci of PDWA, and 5.29 [95% CI, 3.37-8.29] for ≥3 foci of AH). Ten-year BC cumulative incidence was 4.3% for NP, 6.6% for PDWA, and 14.6% for AH vs an expected population cumulative incidence of 2.9%.Conclusions and RelevanceIn this contemporary cohort study of women diagnosed with BBD in the percutaneous biopsy era, overall risk of BC was increased vs the general population (DCIS and invasive cancer combined), similar to that in historical BBD cohorts. Development and validation of pathologic classifications including both BBD severity and multiplicity may enable improved BC risk stratification.

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Automated Quantitative Measures of Terminal Duct Lobular Unit Involution and Breast Cancer Risk

Cited by 11 publications

References 28 publications

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer

Benign Breast Disease and Breast Cancer Risk in the Percutaneous Biopsy Era

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