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”
A research intelligence initiative by Pepkio
pri.pepkio.com/top-papers/cancer-genomics-and-diagnostics/2026/
Topics
deep learning · Drug response prediction · Digital pathology · Transcriptomics imputation · Digital pathology · Gene expression · Digital pathology · Treatment response · cancer · Transcriptomics imputation · Neural networks · Digital pathology · multi-modal learning · Biomarker discovery · Drug response prediction · Tumor microenvironment · image-based transcriptomics · Predictive modeling · Genomic profiling · pathology-transcriptomics integration
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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 2026-07-13. 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