Abstract:
BACKGROUND:Breast ductal carcinoma in situ (DCIS) represent approximately 20% of screen-detected breast cancers. The overall risk for DCIS patients treated with breast-conserving surgery stems almost exclusively from local recurrence. Although a mastectomy or adjuvant radiation can reduce recurrence risk, there are significant concerns regarding patient over-/under-treatment. Current clinicopathological markers are insufficient to accurately assess the recurrence risk. To address this issue, we developed a novel machine learning (ML) pipeline to predict risk of ipsilateral recurrence using digitized whole slide images (WSI) and clinicopathologic long-term outcome data from a retrospectively collected cohort of DCIS patients (n = 344) treated with lumpectomy at Nottingham University Hospital, UK. METHODS:The cohort was split case-wise into training (n = 159, 31 with 10-year recurrence) and validation (n = 185, 26 with 10-year recurrence) sets. The sections from primary tumors were stained with H&E, then digitized and analyzed by the pipeline. In the first step, a classifier trained manually by pathologists was applied to digital slides to annotate the areas of stroma, normal/benign ducts, cancer ducts, dense lymphocyte region, and blood vessels. In the second step, a recurrence risk classifier was trained on eight select architectural and spatial organization tissue features from the annotated areas to predict recurrence risk. RESULTS:The recurrence classifier significantly predicted the 10-year recurrence risk in the training [hazard ratio (HR) = 11.6; 95% confidence interval (CI) 5.3-25.3, accuracy (Acc) = 0.87, sensitivity (Sn) = 0.71, and specificity (Sp) = 0.91] and independent validation [HR = 6.39 (95% CI 3.0-13.8), p < 0.0001;Acc = 0.85, Sn = 0.5, Sp = 0.91] cohorts. Despite the limitations of our cohorts, and in some cases inferior sensitivity performance, our tool showed superior accuracy, specificity, positive predictive value, concordance, and hazard ratios relative to tested clinicopathological variables in predicting recurrences (p < 0.0001). Furthermore, it significantly identified patients that might benefit from additional therapy (validation cohort p = 0.0006). CONCLUSIONS:Our machine learning-based model fills an unmet clinical need for accurately predicting the recurrence risk for lumpectomy-treated DCIS patients.
journal_name
Breast Cancer Resjournal_title
Breast cancer research : BCRauthors
Klimov S,Miligy IM,Gertych A,Jiang Y,Toss MS,Rida P,Ellis IO,Green A,Krishnamurti U,Rakha EA,Aneja Rdoi
10.1186/s13058-019-1165-5subject
Has Abstractpub_date
2019-07-29 00:00:00pages
83issue
1eissn
1465-5411issn
1465-542Xpii
10.1186/s13058-019-1165-5journal_volume
21pub_type
杂志文章abstract:INTRODUCTION:Surgery is currently the definitive treatment for early-stage breast cancer. However, the rate of positive surgical margins remains unacceptably high. The human sodium iodide symporter (hNIS) is a naturally occurring protein in human thyroid tissue, which enables cells to concentrate radionuclides. The hNI...
journal_title:Breast cancer research : BCR
pub_type: 杂志文章
doi:10.1186/bcr3404
更新日期:2013-03-18 00:00:00
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章
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abstract:INTRODUCTION:Human epidermal growth factor 2 (Her2), a receptor tyrosine kinase, is overexpressed in breast cancers. It has been successfully targeted by small molecule kinase inhibitors and by antibodies. Recent clinical data show a synergistic response in patients when two antibodies, trastuzumab and pertuzumab, are ...
journal_title:Breast cancer research : BCR
pub_type: 杂志文章
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更新日期:2011-05-22 00:00:00
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章
doi:10.1186/s13058-020-01264-z
更新日期:2020-03-06 00:00:00
abstract::A new gene associated with a high risk of breast cancer, termed BRCAX, may exist on chromosome 13q. Tumours from multicase Nordic breast cancer families, in which mutations in BRCA1 and BRCA2 had been excluded, were analyzed using comparative genomic hybridization in order to identify a region of interest, which was a...
journal_title:Breast cancer research : BCR
pub_type: 杂志文章,评审
doi:10.1186/bcr290
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章
doi:10.1186/bcr2205
更新日期:2008-01-01 00:00:00
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章,评审
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章
doi:10.1186/s13058-017-0803-z
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journal_title:Breast cancer research : BCR
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章
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更新日期:2005-01-01 00:00:00
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doi:10.1186/s13058-020-01284-9
更新日期:2020-05-13 00:00:00
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章,评审
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更新日期:2000-01-01 00:00:00
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章
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更新日期:2012-07-03 00:00:00
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章
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更新日期:2015-05-13 00:00:00
abstract:INTRODUCTION:Rapamycin, an inhibitor of the serine/threonine kinase target of rapamycin, induces G1 arrest and/or apoptosis. Although rapamycin and its analogues are attractive candidates for cancer therapy, their sensitivities with respect to growth inhibition differ markedly among various cancer cells. Using human br...
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pub_type: 杂志文章
doi:10.1186/bcr1344
更新日期:2005-01-01 00:00:00
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章
doi:10.1186/bcr3580
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章
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pub_type: 杂志文章
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更新日期:2006-01-01 00:00:00
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pub_type: 杂志文章,多中心研究
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journal_title:Breast cancer research : BCR
pub_type: 杂志文章
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更新日期:2011-08-04 00:00:00
abstract::Breast cancer is a heterogeneous disease with varied morphological appearances, molecular features, behavior, and response to therapy. Current routine clinical management of breast cancer relies on the availability of robust clinical and pathological prognostic and predictive factors to support clinical and patient de...
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pub_type: 杂志文章,评审
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更新日期:2010-01-01 00:00:00
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pub_type: 杂志文章
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更新日期:2011-01-20 00:00:00
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journal_title:Breast cancer research : BCR
pub_type: 评论,杂志文章
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更新日期:2005-01-01 00:00:00