ArticleThe Journal of clinical investigation2021
Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma.
Article in The Journal of clinical investigation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 35 citations in OpenAlex.
- Identifying head and neck squamous cell carcinoma: HPV-associated signature by integrating bulk and single-cell sequencing data.Medicine · 2026Article
- Selection Criteria for De-Escalated Chemoradiotherapy for HPV-Related Oropharyngeal Cancer Based on Prognostic Biomarkers or Early Tumor Response to Therapy: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Computational Morphological Assessment of Bladder Cancer Tissue Is Prognostic of Recurrence and Overall Survival Following Transurethral Resection.JCO clinical cancer informatics · 2025Article
- Artificial intelligence-based virtual staining platform for identifying tumor-associated macrophages from hematoxylin and eosin-stained images.European journal of cancer (Oxford, England : 1990) · 2025Article
- Artificial intelligence in digital pathology - time for a reality check.Nature reviews. Clinical oncology · 2025Review
- Spatial distance between tumor and lymphocyte can predict the survival of patients with resectable lung adenocarcinoma.Heliyon · 2024Article
- Extracting interpretable features for pathologists using weakly supervised learning to predict p16 expression in oropharyngeal cancer.Scientific reports · 2024Article
- Moving from conventional to adaptive risk stratification for oropharyngeal cancer.Frontiers in oncology · 2024Review
- Identifying primary tumor site of origin for liver metastases via a combination of handcrafted and deep learning features.The journal of pathology. Clinical research · 2024Article
- Recurrent Oropharyngeal Squamous Cell Carcinomas Maintain Anti-tumor Immunity and Multinucleation Levels Following Completion of Radiation.Head and neck pathology · 2023Article
- Clinical, morphologic and molecular heterogeneity of HPV-associated oropharyngeal cancer.Oncogene · 2023Review
- Predicting HPV association using deep learning and regular H&E stains allows granular stratification of oropharyngeal cancer patients.NPJ digital medicine · 2023Article
- Machine learning driven index of tumor multinucleation correlates with survival and suppressed anti-tumor immunity in head and neck squamous cell carcinoma patients.Oral oncology · 2023Article
- CT radiomic signature predicts survival and chemotherapy benefit in stage I and II HPV-associated oropharyngeal carcinoma.NPJ precision oncology · 2023Article
- A review on deep learning applications in highly multiplexed tissue imaging data analysis.Frontiers in bioinformatics · 2023Review
- Multi-modality artificial intelligence in digital pathology.Briefings in bioinformatics · 2022Review
- Article
- Oropharyngeal cancer outcomes correlate with p16 status, multinucleation and immune infiltration.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2022Article
- The state of the art for artificial intelligence in lung digital pathology.The Journal of pathology · 2022Review
- Artificial intelligence for automating the measurement of histologic image biomarkers.The Journal of clinical investigation · 2021Article
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Authors and funding
24 authors at 10 institutions in 2 countries.
Funding
Abstract
BACKGROUNDPatients with p16+ oropharyngeal squamous cell carcinoma (OPSCC) are potentially cured with definitive treatment. However, there are currently no reliable biomarkers of treatment failure for p16+ OPSCC. Pathologist-based visual assessment of tumor cell multinucleation (MN) has been shown to be independently prognostic of disease-free survival (DFS) in p16+ OPSCC. However, its quantification is time intensive, subjective, and at risk of interobserver variability.METHODSWe present a deep-learning-based metric, the multinucleation index (MuNI), for prognostication in p16+ OPSCC. This approach quantifies tumor MN from digitally scanned H&E-stained slides. Representative H&E-stained whole-slide images from 1094 patients with previously untreated p16+ OPSCC were acquired from 6 institutions for optimization and validation of the MuNI.RESULTSThe MuNI was prognostic for DFS, overall survival (OS), or distant metastasis-free survival (DMFS) in p16+ OPSCC, with HRs of 1.78 (95% CI: 1.37-2.30), 1.94 (1.44-2.60), and 1.88 (1.43-2.47), respectively, independent of age, smoking status, treatment type, or tumor and lymph node (T/N) categories in multivariable analyses. The MuNI was also prognostic for DFS, OS, and DMFS in patients with stage I and stage III OPSCC, separately.CONCLUSIONMuNI holds promise as a low-cost, tissue-nondestructive, H&E stain-based digital biomarker test for counseling, treatment, and surveillance of patients with p16+ OPSCC. These data support further confirmation of the MuNI in prospective trials.FUNDINGNational Cancer Institute (NCI), NIH; National Institute for Biomedical Imaging and Bioengineering, NIH; National Center for Research Resources, NIH; VA Merit Review Award from the US Department of VA Biomedical Laboratory Research and Development Service; US Department of Defense (DOD) Breast Cancer Research Program Breakthrough Level 1 Award; DOD Prostate Cancer Idea Development Award; DOD Lung Cancer Investigator-Initiated Translational Research Award; DOD Peer-Reviewed Cancer Research Program; Ohio Third Frontier Technology Validation Fund; Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering; Clinical and Translational Science Award (CTSA) program, Case Western Reserve University; NCI Cancer Center Support Grant, NIH; Career Development Award from the US Department of VA Clinical Sciences Research and Development Program; Dan L. Duncan Comprehensive Cancer Center Support Grant, NIH; and Computational Genomic Epidemiology of Cancer Program, Case Comprehensive Cancer Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, the US Department of VA, the DOD, or the US Government.
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