Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer.
Background: There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from com...
| Publicado en: | Breast Cancer Research Vol. 18; pp. 1 - 12 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
| Formato: | research randomized controlled trial Journal Article |
| Publicado: |
BioMed Central
2/16/2016
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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=113085758&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113085758 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14655411 8UYJ jtl: Breast Cancer Research issn: 14655411 maglogo: N pubinfo: dt: 2/16/2016 vid: 18 pid: 24147 pub: BioMed Central artinfo: ui: 113085758 113085758 NLM26882907 113085758 10.1186/s13058-016-0682-8 NLM26882907 PMC4755003 113085758 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer. aug: au: Ali, H. Raza Dariush, Aliakbar Provenzano, Elena Bardwell, Helen Abraham, Jean E. Iddawela, Mahesh Vallier, Anne-Laure Hiller, Louise Dunn, Janet. A. Bowden, Sarah J. Hickish, Tamas McAdam, Karen Houston, Stephen Irwin, Mike J. Pharoah, Paul D. P. Brenton, James D. Walton, Nicholas A. Earl, Helena M. Caldas, Carlos affil: Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK sug: subj: Chemotherapy, Adjuvant Methods Lymphocytes Pathology Neoadjuvant Therapy Methods Breast Neoplasms Drug Therapy Hydrocarbons Administration and Dosage Breast Neoplasms Blood Adult Receptors, Cell Surface Human Aged Female Middle Age Breast Neoplasms Pathology Antineoplastic Agents, Combined Administration and Dosage Epirubicin Administration and Dosage Biopsy Validation Studies Comparative Studies Evaluation Research Multicenter Studies Funding Source Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Female ab: Background: There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy.Methods: We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression.Results: Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553).Conclusions: A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer.Trial Registration: ClinicalTrials.gov NCT00070278 ; 03/10/2003. pubtype: Academic Journal doctype: research randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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