Supplementary Materials for Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling

By Yinyin Yuan, H. Failmezger, O. Rueda, H. R. Ali, S. Gräf, Roland F. Schwarz, C. Curtis, M. Dunning, H. Bardwell, Sarah Doyle and 3 more. 2012

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Citations in the corpus

Multi-class texture analysis in colorectal cancer histology (2016, methodology): “This approach has been successfully used by various groups (see Xu et al. 3 for an overview) and has led to new candidate biomarkers 4–6 .”
T Cell Dysfunction in Cancer (2018, result): “Similar findings have been obtained in breast cancer (Heindl et al., 2015; Yuan et al., 2012).”
From big data analysis to personalized medicine for all: challenges and opportunities (2015, background): “Even histopathological and radiological images which are traditionally evaluated and scored by trained experts are now subjected to computational quantifications (i.e. imaging informatics) [10 – 12 , 33].”
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study (2019, unknown): “Machinelearningmethodscanextractprognosticatorsfrom suchimages[12] andhavealso beenusedto extractprognosticatorsfrom radiologicalimages[13].”
A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images (2016, background): “in [31] found that the spatial arrangement of stromal cell in tumors is a prognostic factor in breast cancer.”
Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images (2018, unknown): “[34] performed nuclear segmentation and then classified”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, unknown): “Multiple statistical andmachine learningmethods have been applied for this purpose including consensus clustering [250], linear classifier [251], LASSO regressionmodeling [252],”
Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. (2017, unknown): “Through the integration of gene expression and somatic copy number data with automated microenvironment analysis from standard hematoxylin and eosin slides, Yuan and colleagues demonstrated that survival predictions in ER negative breast cancer can be optimized (Yuan et al., 2012).”
Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype (2018, background): “These approaches have largely focused on hand-crafted, user-designed features, such as statistics of shape and color, to capture cell-by cell morphology, which are difficult to adapt to new data sets.(5,6) Prior work on automated grading addresses mitotic count,(7) nuclear atypia,(8) and tubule form”
Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks (2020, background): “Compare with genomics data, pathological images can present tumor growth and morphology in extremely detailed, gigapixel resolution which is extremely useful for cancer study [6, 7].”
Breast Cancer Histopathology Image Analysis: A Review (2014, unknown): “In a recent study [115], the authors used an image analysis approach to derive knowledge about the tumor cellular composition (percentage of cancer, stromal and lymphocytic nuclei), which they used to correct copy number data and more accurately estimate the HER2 amplification.”