Literature DB >> 22072638

Systematic analysis of breast cancer morphology uncovers stromal features associated with survival.

Andrew H Beck1, Ankur R Sangoi, Samuel Leung, Robert J Marinelli, Torsten O Nielsen, Marc J van de Vijver, Robert B West, Matt van de Rijn, Daphne Koller.   

Abstract

The morphological interpretation of histologic sections forms the basis of diagnosis and prognostication for cancer. In the diagnosis of carcinomas, pathologists perform a semiquantitative analysis of a small set of morphological features to determine the cancer's histologic grade. Physicians use histologic grade to inform their assessment of a carcinoma's aggressiveness and a patient's prognosis. Nevertheless, the determination of grade in breast cancer examines only a small set of morphological features of breast cancer epithelial cells, which has been largely unchanged since the 1920s. A comprehensive analysis of automatically quantitated morphological features could identify characteristics of prognostic relevance and provide an accurate and reproducible means for assessing prognosis from microscopic image data. We developed the C-Path (Computational Pathologist) system to measure a rich quantitative feature set from the breast cancer epithelium and stroma (6642 features), including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. These measurements were used to construct a prognostic model. We applied the C-Path system to microscopic images from two independent cohorts of breast cancer patients [from the Netherlands Cancer Institute (NKI) cohort, n = 248, and the Vancouver General Hospital (VGH) cohort, n = 328]. The prognostic model score generated by our system was strongly associated with overall survival in both the NKI and the VGH cohorts (both log-rank P ≤ 0.001). This association was independent of clinical, pathological, and molecular factors. Three stromal features were significantly associated with survival, and this association was stronger than the association of survival with epithelial characteristics in the model. These findings implicate stromal morphologic structure as a previously unrecognized prognostic determinant for breast cancer.

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Year:  2011        PMID: 22072638     DOI: 10.1126/scitranslmed.3002564

Source DB:  PubMed          Journal:  Sci Transl Med        ISSN: 1946-6234            Impact factor:   17.956


  257 in total

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Review 10.  Machine Learning in Medicine.

Authors:  Rahul C Deo
Journal:  Circulation       Date:  2015-11-17       Impact factor: 29.690

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