ArticleNPJ digital medicine2025
Evidential deep learning-based ALK-expression screening using H&E-stained histopathological images.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- AI-Powered TAT-Net Hyperspectral Imaging for Rapid and Accurate Differentiation of Sinonasal Inverted Papilloma and Malignancy.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Validation of histopathology-based deep learning algorithms for detection of actionable non-small cell lung cancer biomarkers.NPJ precision oncology · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Efficient and accurate identification of genetic alterations of non-small cell lung cancer is a critical diagnostic process for targeted therapies. Utilizing advanced modern deep learning is a potential solution that can accurately predict genetic alterations from H&E-stained pathological images without additional testing procedures and costs. However, clinically applicable predictive power for Anaplastic Lymphoma Kinase (ALK) rearrangement has yet to succeed. To tackle these issues, we have developed a pathologically interpretable, evidence-based deep learning algorithm to screen ALK alterations to reduce unnecessary medical costs and understand the association between genetic alterations and pathological phenotypes. The proposed model resulted in +95% accuracy with both resection and biopsy datasets, which can be applicable in the clinic. The deep learning approach can maximize the benefits for screening genetic alterations as well as provide the most clinical utility. A stand-alone Python-based open-source software package is publicly available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.