Predicting tumour mutational burden from histopathological images using multiscale deep learning
Open accessPublished version of a preprint
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Numerous studies have demonstrated potential for AI to predict immunotherapy biomarkers from H&E images of NSCLC, including TMB, PD-L1 expression, and TILs.
“Numerous studies have demonstrated the potential for using AI to predict immunotherapy biomarkers from H&E images of patients with NSCLC, including tumor mutation burden (TMB; ref. 76), PD-L1 expression (105), and TILs (106, 107).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [76]
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References 26 · Cited by 103
- Authors
- Jain, M. S., & Massoud, T. F.
- Affiliations
- Stanford University (US)
- Corresponding author
- Jain, Mika S.
- Massoud, Tarik F.
- Venue
- Nature Machine Intelligence
- Published
- 2020-06-15
- Volume
- 2
- Issue
- 6
- Pages
- 356-362
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3035105468
- Cited by
- 103
- References
- 26
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 21 · 2023: 22 · 2022: 24 · 2021: 12
- FWCI
- 5.70
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Keywords
- tumor mutational burden, histopathological images, lung adenocarcinoma, H&E staining, immunotherapy response biomarkers, genotype-phenotype correlation
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