A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics
Most Cited Paper
in Cancer Genomics and Diagnostics
18 Months Post-Publication
Class of 2026
“A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics”
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Topics
deep learning · cancer treatment response prediction · Histopathology images · transcriptomics imputation · Gene expression prediction from histology · Spatial transcriptomics · Convolutional neural network · treatment outcome prediction · digital pathology · Gene expression prediction from histology · Multi-modal learning · computational pathology · therapy response biomarkers · Gene expression prediction from histology · precision oncology
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Pepkio Research Index (PRI). Topics and Trends in Most Cited Cancer Genomics and Diagnostics Papers, Class of 2026. https://pri.pepkio.com/top-papers/cancer-genomics-and-diagnostics/2026. Accessed 2026-08-20. Methodology Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459