Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

By Joel H. Saltz, Rajarsi R. Gupta, Le Hou, Tahsin M. Kurç, P. Singh, Vu Nguyen, Dimitris Samaras, K. Shroyer, Tianhao Zhao, Rebecca C. Batiste and 6 more. Cell Reports, 2018

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SUMMARY Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment.

Subfields: Biophysics, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/0e8b061e08eb1ac8968e44edc0e54da658afad0e

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Citations in the corpus

AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, positive): “These datasets can be correlated to molecular tumor characteristics and can be used to quantitatively characterize tissue at multiple spatial scales to create biomarkers that predict outcome and treatment response [150], [152]–[154].”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “spatial patterns of arrangement of lymphocytes) to perform prognosis and classification tasks [220], [221], [222].”
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs (2023, methodology): “TCGA-TIL [45, 46] contains 2 , 480 image patches (500 × 500px) that were partitioned from H&E whole-slide images from The Cancer Genome Atlas (TCGA) [47] lung adenocarcinoma (LUAD) cases (5.9% of the patches are labelled as LUAD).”
Artificial intelligence for digital and computational pathology (2023, neutral): “8 These authors contributed equally: Andrew H. Song, Guillaume Jaume. e-mail: FaisalMahmood@ bwh.harvard.edu”
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images (2021, background): “…the entire composition of a WSI and dependent on the interactions of its components ( i.e. - a ﬁne-grained visual recognition problem such as one that involves understanding the complex milieu of stroma, tumor aggregates, immune cells and other visual concepts) [68, 7, 47, 22, 40, 21, 6, 39, 15].”
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, background): “On the other hand, Saltz et al. [151] applied a simple threshold to the heatmap to locate each lymphocyte.”