ArticleLife (Basel, Switzerland)2025
Artificial Intelligence (AI) for Programmed Death Ligand-1 (PD-L1) Immunohistochemical Assessment in Urothelial Carcinomas: "Teaching" Cell Differentiation to AI Systems.
Article in Life (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
Assessment of Programmed Death-Ligand 1 (PD-L1) immunohistochemical (IHC) expression on tumor cells (TCs) and immune cells (ICs) in bladder cancer (BC) is challenging. Artificial Intelligence (AI) has potential for accurate PD-L1 IHC scoring, but its efficiency remains debatable. Our aim was to compare two AI protocols provided by the free QuPath software (v0.5.1) (Selected Area Interpretation (AI-SAI) and Whole Slide Imaging (AI-WSI)) with manual PD-L1 IHC scoring. A total of 43 BCs were included. PD-L1 IHC was performed using the SP263 clone. The IHC slides were digitized and further imported into QuPath. The PD-L1 positivity threshold was set at 25%. Statistically significant correlations were observed between AI-SAI and manual interpretation for both TCs (r = 0.85) and ICs (r = 0.57). AI-WSI yielded comparable results, with correlation coefficients of r = 0.82 for TCs and r = 0.56 for ICs. However, AI-SAI demonstrated stronger agreement with manual assessment (κ = 0.86) compared to AI-WSI (κ = 0.65). Receiver Operating Characteristic (ROC) analysis further supported the superiority of AI-SAI, with higher AUC values for both TCs (0.96 vs. 0.92) and ICs (0.92 vs. 0.90). Our findings indicate that AI-SAI is preferable to AI-WSI, particularly in BC cases with high PD-L1-positive TC content. Nevertheless, supervision by an experienced pathologist is mandatory.
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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 Socratic graph.