Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab
Open accessAbstract
Assessment of programmed death ligand 1 (PD-L1) expression by immunohistochemistry (IHC) has emerged as an important predictive biomarker across multiple tumor types. However, manual quantitation of PD-L1 positivity can be difficult and leads to substantial inter-observer variability. Although the development of artificial intelligence (AI) algorithms may mitigate some of the challenges associated with manual assessment and improve the accuracy of PD-L1 expression scoring, use of AI-based approaches to oncology biomarker scoring and drug development has been sparse, primarily due to the lack of large-scale clinical validation studies across multiple cohorts and tumor types. We developed AI-powered algorithms to evaluate PD-L1 expression on tumor cells by IHC and compared it with manual IHC scoring in urothelial carcinoma, non-small cell lung cancer, melanoma, and squamous cell carcinoma of the head and neck (prospectively determined during the phase II and III CheckMate clinical trials). 1,746 slides were retrospectively analyzed, the largest investigation of digital pathology algorithms on clinical trial datasets performed to date. AI-powered quantification of PD-L1 expression on tumor cells identified more PD-L1-positive samples compared with manual scoring at cutoffs of ≥1% and ≥5% in most tumor types. Additionally, similar improvements in response and survival were observed in patients identified as PD-L1-positive compared with PD-L1-negative using both AI-powered and manual methods, while improved associations with survival were observed in patients with certain tumor types identified as PD-L1-positive using AI-powered scoring only. Our study demonstrates the potential for implementation of digital pathology-based methods in future clinical practice to identify more patients who would benefit from treatment with immuno-oncology therapy compared with current guidelines using manual assessment.
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In dermatopathology, AI applications for IHC quantification include assessing proliferation markers (e.g., Ki-67) in melanocytic lesions, quantifying lineage markers (Melan-A, SOX10) for melanocytic lesion evaluation, and analyzing immune-related markers (CD3, CD8, PD-L1) in cutaneous tumors and inflammatory dermatoses.
“In dermatopathology, applications include assessing proliferation markers (e.g., Ki-67) in melanocytic lesions [37], quantifying lineage markers (Melan-A, SOX10) [38] for melanocytic lesion evaluation, and analyzing immune-related markers (CD3, CD8, PD-L1) [39,40] in both cutaneous tumors and inflammatory dermatoses.”
From Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature (2026) · cites this paper as [40]
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Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review
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Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature
Abu-Dayeh, A., Cazzato, G., & Giubellino, A. (2026) · Diagnostics · doi:10.3390/diagnostics16111702 · Cited by 1 · see citing works
“…applications include assessing proliferation markers (e.g., Ki-67) in melanocytic lesions [37], quantifying lineage markers (Melan-A, SOX10) [38] for melanocytic lesion evaluation, and analyzing immune-related markers (CD3, CD8, PD-L1) [39,40] in both cutaneous tumors and inflammatory dermatoses.”
- Authors
- Baxi, V., Lee, G., Duan, C., Pandya, D., Cohen, D. N., Edwards, R., Chang, H., Li, J., Elliott, H., Pokkalla, H., Glass, B., Agrawal, N., Lahiri, A., Wang, D., Khosla, A., Wapinski, I., Beck, A., & Montalto, M.
- Affiliations
- Bristol-Myers Squibb (United States) (US)
- PathAI (United States) (US)
- Corresponding author
- Baxi, Vipul
- ORCID
- Venue
- Modern Pathology
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 35
- Issue
- 11
- Pages
- 1529-1539
- ISSN
- 0893-3952 (ISSN-L)
- PMC
- PMC9596372
- OpenAlex
- W4285589888
- Cited by
- 66
- References
- 40
- Citations by year
- 2026: 16 · 2025: 23 · 2024: 12 · 2023: 13 · 2022: 2
- FWCI
- 5.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Modern Pathology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Bristol-Myers Squibb
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- 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
- Keywords
- PD-L1 expression, artificial intelligence, immunohistochemistry scoring, digital pathology, nivolumab, ipilimumab, immune checkpoint inhibitors, urothelial carcinoma, non-small cell lung cancer, melanoma, head and neck squamous cell carcinoma, predictive biomarkers, interobserver variability, treatment response, survival outcomes
- MeSH terms
- Ipilimumab, Nivolumab, Antibodies, Monoclonal, Artificial Intelligence, Urinary Bladder Neoplasms, Carcinoma, Non-Small-Cell Lung, Carcinoma, Transitional Cell, Humans, Lung Neoplasms, Retrospective Studies, Biomarkers, Tumor, B7-H1 Antigen
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