ReviewThe Journal of pathology2026
What practicing pathologists and oncologists should know about the new computational pathology-based companion diagnostic tools.
Review in The Journal of pathology, 2026. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Causal AI for cancer immunotherapy: a narrative framework review of target trial emulation, treatment-effect learning and clinical translation.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
The integration of artificial intelligence into pathology is transforming the assessment of histological and immunohistochemical (IHC) slides, offering opportunities to reduce variability and streamline diagnostics. In practical terms, most available tools and research models emulate the diagnostic capabilities of pathologists by detecting, grading, and classifying tumours and other diseases. More recent applications have moved beyond mimicry, aiming to predict established biomarkers, such as microsatellite instability or IHC-based markers, and to tackle even more ambitious tasks, such as directly predicting patient prognosis from H&E whole slide images. Remarkably, novel computational tools are now being designed as companion diagnostic assays, linking the automated evaluation of specific IHC biomarkers to the prediction of response to specific drugs, potentially marking a new chapter in the evolution of digital and computational pathology. The TROPION-PanTumor01 trial recently demonstrated the superiority of a supervised machine learning model (termed the quantitative continuous score [QCS] by the vendor) in assessing TROP2 IHC compared with human scoring, promising better stratification of patients with non-small cell lung cancer for treatment with datopotamab deruxtecan. The same approach has shown promise in refining HER2 (human epidermal growth factor receptor 2) and PD-L1 (programmed death-ligand 1) evaluations, revealing patient subgroups that may benefit from targeted therapies. Moreover, other similar approaches are progressively reaching the market, posing significant opportunities and challenges for clinicians involved in the care of patients with cancer. This Perspective is promoted by the European Society of Digital and Integrative Pathology (ESDIP, founded in 2016, and having long-standing experience in computational pathology, esdipath.org) and the European Interdisciplinary Society of Artificial Intelligence for Cancer Research (ESAC, a recently established initiative, founded in 2024, esac-network.eu), both bringing together clinicians, engineers and other professionals dedicated to the development and clinical translation of computational approaches aimed at improving patient care. It aims to provide an informed overview of novel computational pathology companion diagnostic tools, with a particular focus on the background that practicing pathologists and oncologists need to have with these tools, when transitioning from research to clinical practice, irrespective of their prior familiarity with computational approaches. © 2026 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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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.