# A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics

*PRI Rank #20 · Topics and Trends in Most Cited Cancer Genomics and Diagnostics Papers, Class of 2026*

*Canonical URL: https://pri.pepkio.com/top-papers/cancer-genomics-and-diagnostics/2026/rank-20*

| Field | Value |
| --- | --- |
| Rank | #20 |
| 18m citations | 88 |
| Journal | Nature Cancer |
| Year | 2024 |
| DOI | 10.1038/s43018-024-00793-2 |
| Corresponding authors | Danh-Tai Hoang, Eric A. Stone, Eytan Ruppin |
| Institution | Australian National University, Australia |

**Ranking page:** [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)

**Paper link:** [10.1038/s43018-024-00793-2](https://doi.org/10.1038/s43018-024-00793-2)

## 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

## Cite this ranking

```
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 2026-07-14.

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
```