Fig. 2: Workflow of the data preparation and experiments. | npj Precision Oncology

Fig. 2: Workflow of the data preparation and experiments.

From: Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer

Fig. 2: Workflow of the data preparation and experiments.

A Tissue preparation: The cohorts were digitized and represented in the form of patches extracted from whole slide images (WSIs) and tissue microarray (TMA) punches. B Image preprocessing: A subset composed of H&E-stained TMA and corresponding immunofluorescence (IF) images were utilized to analyze tumor-infiltrating lymphocyte (TIL) subtypes. C Cell Identification: Corresponding TILs from the H&E samples were associated with IF molecular labels (CD4+, CD8+, CD20+). D Feature extraction: Phenotyping features were extracted from the TIL cells patches extracted from the WSI and TMAs. E Single-Cell Clustering: An unsupervised clustering approach was applied to the phenotyping features of TILs. F Molecular Assessment: RNA-seq-based transcriptome data is obtained from each WSI-TCGA sample. TIL clusters were used as the input matrix to build a model that associate with the clinical outcome overall survival, using a Cox proportional hazards regression model with elastic net regularization. Associations between cluster conformation, molecular, morphological and genomics composition were studied. Minor components of the figure were obtained from BioRender.

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