A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk.
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 recurrenc...
| Publicado en: | Breast Cancer Research Vol. 21; no. 1 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
7/29/2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137769651&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137769651 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14655411 8UYJ jtl: Breast Cancer Research issn: 14655411 maglogo: N pubinfo: dt: 7/29/2019 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 137769651 137769651 NLM31358020 10.1186/s13058-019-1165-5 NLM31358020 137769651 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk. aug: au: Klimov, Sergey Miligy, Islam M. Gertych, Arkadiusz Jiang, Yi Toss, Michael S. Rida, Padmashree Ellis, Ian O. Green, Andrew Krishnamurti, Uma Rakha, Emad A. Aneja, Ritu affil: Department of Biology, Georgia State University, 30303, Atlanta, GA, USA sug: subj: Immunohistochemistry Adenocarcinoma Metabolism Breast Neoplasms Metabolism Adenocarcinoma Pathology Breast Neoplasms Pathology Aged Neoplasm Grading Prognosis Middle Age Adult Risk Assessment Breast Neoplasms Therapy Cox Proportional Hazards Model Neoplasm Staging Female Mastectomy Breast Neoplasms Mortality Adenocarcinoma Therapy Aged, 80 and Over Neoplasm Recurrence, Local Scales Aged: 65+ years Middle Aged: 45-64 years Adult: 19-44 years Aged, 80 & over Female ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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