Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival

By Andrew H. Beck, Ankur R. Sangoi, S. Leung, Robert J. Marinelli, Torsten O. Nielsen, M. J. van de Vijver, Robert B. West, M. van de Rijn, Daphne Koller. Science Translational Medicine, 2011

747 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/077592c2b76318a15562cf9f962f515988c011fb

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Citations in the corpus

Economics, Fairness and Algorithmic Bias (2020, neutral): “…(Highhouse, 2008), student performance (Dawes, 1971, 1979), criminal defendants’ recidivism (Thompson, 1952; Wormith and Goldstone, 1984), demand for products (Schweitzer and Cachon, 2000) and medical diagnoses (Adams et al., 1986; Beck et al., 2011; Dawes et al., 1989; Grove et al., 2000).”
Applications of machine learning in drug discovery and development (2019, background): “Beck et al. 88 looked at cells in the context of their spatial locations within the surrounding tumour stroma and showed associations between stromal features and survival in breast cancer.”
Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. (2018, background): “prognostic histological features in breast cancer.(3) More recently, in clinical cardiology, it has been shown that ML is more adept in the prediction of either cardiovascular or all-cause mortality than clinical or imaging modalities used separately.”
A guide to deep learning in healthcare (2019, methodology): “Combined with pixel-level analyses, CNNs have even been used to discover biological features of tissue associated with survival probability 21 .”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, positive): “In these medical applications, the gold standard is to evaluate models on an independent test set collected from a different hospital (e.g.,Beck et al. (2011); Liu et al. (2017); Courtiol et al. (2019); McKinney et al. (2020)) or at least with a different scanner within the same hospital (e.g.,…”
Deep Learning for Identifying Metastatic Breast Cancer (2016, methodology): “Building on this framework, approaches have been developed for the automated extraction of moderately high dimensional sets of image features (on the order of thousands) from histopathological images followed by the construction of relatively simple, linear classification models using methods design”
Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases (2016, result): “In addition, recent work by Beck et al.[44] suggest that histologic patterns within the stroma might be critical in predicting overall survival and outcome in breast cancer patients.”
Image analysis and machine learning in digital pathology: Challenges and opportunities (2016, neutral): “Work by Beck et al. (Beck et al., 2011) has implicated stromal features in the prognosis of cancers.”
A Multi-Organ Nucleus Segmentation Challenge (2020, methodology): “Recently, computer based assessment of tissue images has been used for tumor molecular sub-type detection [19], mortality or recurrence prediction [9], [20], and treatment”
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features (2016, background): “Current clinical practice could thus beneﬁt greatly from the development and incorporation of such systems into clinical care 31,32 .”
Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association (2019, background): “For example, Beck et al examined ‘a rich quantitative feature set’ consisting of 6642 engineered features expressing characteristics of both breast cancer epithelium and stroma [19] and correlated these to patient outcome.”
Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them (2018, neutral): “…(Dawes, 1971; Dawes, 1979), prisoners’ likelihood of recidivism (Thompson, 1952; Wormith & Goldstone, 1984), medical diagnoses (Adams et al., 1986; Beck et al., 2011; Dawes, Faust, & Meehl, 1989; Grove et al., 2000), demand for products (Schweitzer & Cachon, 2000), and so on (for reviews see…”