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Koyuncu CF, Frederick MJ, Thompson LDR, Corredor G, Khalighi S, Zhang Z, Song B, Lu C, Nag R, Sankar Viswanathan V, Gilkey M, Yang K, Koyfman SA, Chute DJ, Castro P, Lewis JS, Madabhushi A, Sandulache VC. Machine learning driven index of tumor multinucleation correlates with survival and suppressed anti-tumor immunity in head and neck squamous cell carcinoma patients. Oral Oncol 2023; 143:106459. [PMID: 37307602 DOI: 10.1016/j.oraloncology.2023.106459] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/28/2023] [Accepted: 06/05/2023] [Indexed: 06/14/2023]
Abstract
OBJECTIVES Matching treatment intensity to tumor biology is critical to precision oncology for head and neck squamous cell carcinoma (HNSCC) patients. We sought to identify biological features of tumor cell multinucleation, previously shown by us to correlate with survival in oropharyngeal (OP) SCC using a machine learning approach. MATERIALS AND METHODS Hematoxylin and eosin images from an institutional OPSCC cohort formed the training set (DTr). TCGA HNSCC patients (oral cavity, oropharynx and larynx/hypopharynx) formed the validation set (DV). Deep learning models were trained in DTr to calculate a multinucleation index (MuNI) score. Gene set enrichment analysis (GSEA) was then used to explore correlations between MuNI and tumor biology. RESULTS MuNI correlated with overall survival. A multivariable nomogram that included MuNI, age, race, sex, T/N stage, and smoking status yielded a C-index of 0.65, and MuNI was prognostic of overall survival (2.25, 1.07-4.71, 0.03), independent of the other variables. High MuNI scores correlated with depletion of effector immunocyte subsets across all HNSCC sites independent of HPV and TP53 mutational status although the correlations were strongest in wild-type TP53 tumors potentially due to aberrant mitotic events and activation of DNA-repair mechanisms. CONCLUSION MuNI is associated with survival in HNSCC across subsites. This may be driven by an association between high levels of multinucleation and a suppressive (potentially exhausted) tumor immune microenvironment. Mechanistic studies examining the link between multinucleation and tumor immunity will be required to characterize biological drivers of multinucleation and their impact on treatment response and outcomes.
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Affiliation(s)
- Can F Koyuncu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Mitchell J Frederick
- Bobby R. Alford Department of Otolaryngology- Head and Neck Surgery, Baylor College of Medicine, Houston, TX, United States
| | | | - Germán Corredor
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Sirvan Khalighi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Zelin Zhang
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Bolin Song
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Cheng Lu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Reetoja Nag
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Vidya Sankar Viswanathan
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Michael Gilkey
- Atlanta Veterans Administration Medical Center, Atlanta, GA, United States
| | - Kailin Yang
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic Foundation, Cleveland OH, United States
| | - Shlomo A Koyfman
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic Foundation, Cleveland OH, United States
| | - Deborah J Chute
- Department of Pathology, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Patricia Castro
- Department of Pathology, Baylor College of Medicine, Houston, TX, United States
| | - James S Lewis
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Otolaryngology - Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States; Atlanta Veterans Administration Medical Center, Atlanta, GA, United States.
| | - Vlad C Sandulache
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States; ENT Section, Operative Care Line, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, United States; Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, United States.
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2
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Chen Y, Li H, Janowczyk A, Toro P, Corredor G, Whitney J, Lu C, Koyuncu CF, Mokhtari M, Buzzy C, Ganesan S, Feldman MD, Fu P, Corbin H, Harbhajanka A, Gilmore H, Goldstein LJ, Davidson NE, Desai S, Parmar V, Madabhushi A. Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer. NPJ Breast Cancer 2023; 9:40. [PMID: 37198173 PMCID: PMC10192429 DOI: 10.1038/s41523-023-00545-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 04/28/2023] [Indexed: 05/19/2023] Open
Abstract
Prognostic markers currently utilized in clinical practice for estrogen receptor-positive (ER+) and lymph node-negative (LN-) invasive breast cancer (IBC) patients include the Nottingham grading system and Oncotype Dx (ODx). However, these biomarkers are not always optimal and remain subject to inter-/intra-observer variability and high cost. In this study, we evaluated the association between computationally derived image features from H&E images and disease-free survival (DFS) in ER+ and LN- IBC. H&E images from a total of n = 321 patients with ER+ and LN- IBC from three cohorts were employed for this study (Training set: D1 (n = 116), Validation sets: D2 (n = 121) and D3 (n = 84)). A total of 343 features relating to nuclear morphology, mitotic activity, and tubule formation were computationally extracted from each slide image. A Cox regression model (IbRiS) was trained to identify significant predictors of DFS and predict a high/low-risk category using D1 and was validated on independent testing sets D2 and D3 as well as within each ODx risk category. IbRiS was significantly prognostic of DFS with a hazard ratio (HR) of 2.33 (95% confidence interval (95% CI) = 1.02-5.32, p = 0.045) on D2 and a HR of 2.94 (95% CI = 1.18-7.35, p = 0.0208) on D3. In addition, IbRiS yielded significant risk stratification within high ODx risk categories (D1 + D2: HR = 10.35, 95% CI = 1.20-89.18, p = 0.0106; D1: p = 0.0238; D2: p = 0.0389), potentially providing more granular risk stratification than offered by ODx alone.
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Grants
- R01 CA216579 NCI NIH HHS
- C06 RR012463 NCRR NIH HHS
- R43 EB028736 NIBIB NIH HHS
- U01 CA239055 NCI NIH HHS
- R01 CA249992 NCI NIH HHS
- UG1 CA233328 NCI NIH HHS
- R01 CA220581 NCI NIH HHS
- R01 CA202752 NCI NIH HHS
- R01 CA208236 NCI NIH HHS
- U01 CA248226 NCI NIH HHS
- I01 BX004121 BLRD VA
- R01 CA257612 NCI NIH HHS
- U54 CA254566 NCI NIH HHS
- Research reported in this study was supported by the National Cancer Institute under award numbers R01CA249992-01A1, R01CA202752-01A1, R01CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1, R01CA257612-01A1, 1U01CA239055-01, 1U01CA248226-01, 1U54CA254566-01, National Heart, Lung and Blood Institute 1R01HL15127701A1, R01HL15807101A1, National Institute of Biomedical Imaging and Bioengineering 1R43EB028736-01, National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service the Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program (W81XWH-19-1-0668), the Prostate Cancer Research Program (W81XWH-15-1-0558, W81XWH-20-1-0851), the Lung Cancer Research Program (W81XWH-18-1-0440, W81XWH-20-1-0595), the Peer Reviewed Cancer Research Program (W81XWH-18-1-0404, W81XWH-21-1-0345, W81XWH-21-1-0160), the Kidney Precision Medicine Project (KPMP) Glue Grant and sponsored research agreements from Bristol Myers-Squibb, Boehringer-Ingelheim, Eli-Lilly and Astrazeneca.
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Affiliation(s)
- Yuli Chen
- Shaanxi Normal University, School of Computer Science, Xi'an, China
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Haojia Li
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Andrew Janowczyk
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Precision Oncology Center, University of Lausanne, Lausanne, Switzerland
| | - Paula Toro
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Germán Corredor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Jon Whitney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Cheng Lu
- Shaanxi Normal University, School of Computer Science, Xi'an, China
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Can F Koyuncu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Mojgan Mokhtari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Christina Buzzy
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Shridar Ganesan
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Michael D Feldman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, School of Medicine, Cleveland, OH, USA
| | - Haley Corbin
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | - Hannah Gilmore
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | - Nancy E Davidson
- Fred Hutchinson Cancer Research Center, University of Washington, and Seattle Cancer Care Alliance, Seattle, WA, USA
| | - Sangeeta Desai
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Vani Parmar
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Anant Madabhushi
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Louis Stokes VA Medical Center, Cleveland, OH, USA.
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3
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Koyuncu CF, Nag R, Lu C, Corredor G, Viswanathan VS, Sandulache VC, Fu P, Yang K, Pan Q, Zhang Z, Xu J, Chute DJ, Thorstad WL, Faraji F, Bishop JA, Mehrad M, Castro PD, Sikora AG, Thompson LD, Chernock RD, Lang Kuhs KA, Wasman JK, Luo JR, Adelstein DJ, Koyfman SA, Lewis Jr JS, Madabhushi A. Image analysis reveals differences in tumor multinucleations in Black and White patients with human papillomavirus-associated oropharyngeal squamous cell carcinoma. Cancer 2022; 128:3831-3842. [PMID: 36066461 PMCID: PMC9782693 DOI: 10.1002/cncr.34446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/17/2022] [Accepted: 06/28/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Understanding biological differences between different racial groups of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) patients, who have differences in terms of incidence, survival, and tumor morphology, can facilitate accurate prognostic biomarkers, which can help develop personalized treatment strategies. METHODS This study evaluated whether there were morphologic differences between HPV-associated tumors from Black and White patients in terms of multinucleation index (MuNI), an image analysis-derived metric that measures density of multinucleated tumor cells within epithelial regions on hematoxylin-eosin images and previously has been prognostic in HPV-associated OPSCC patients. In this study, the authors specifically evaluated whether the same MuNI cutoff that was prognostic of overall survival (OS) and disease-free survival in their previous study, TTR , is valid for Black and White patients, separately. We also evaluated population-specific cutoffs, TB for Blacks and TW for Whites, for risk stratification. RESULTS MuNI was statistically significantly different between Black (mean, 3.88e-4; median, 3.67e-04) and White patients (mean, 3.36e-04; median, 2.99e-04), with p = .0078. Using TTR , MuNI was prognostic of OS in the entire population with hazard ratio (HR) of 1.71 (p = .002; 95% confidence interval [CI], 1.21-2.43) and in White patients with HR of 1.72 (p = .005; 95% CI, 1.18-2.51). Population-specific cutoff, TW , yielded improved HR of 1.77 (p = .003; 95% CI, 1.21-2.58) for White patients, whereas TB did not improve risk-stratification in Black patients with HR of 0.6 (p = .3; HR, 0.6; 95% CI, 0.2-1.80). CONCLUSIONS Histological difference between White and Black patient tumors in terms of multinucleated tumor cells suggests the need for considering population-specific prognostic biomarkers for personalized risk stratification strategies for HPV-associated OPSCC patients.
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Affiliation(s)
- Can F. Koyuncu
- Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaGeorgiaUSA,Louis Stokes Cleveland Veterans Affairs Medical CenterClevelandOhioUSA
| | - Reetoja Nag
- Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaGeorgiaUSA
| | - Cheng Lu
- Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaGeorgiaUSA
| | - Germán Corredor
- Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaGeorgiaUSA,Louis Stokes Cleveland Veterans Affairs Medical CenterClevelandOhioUSA
| | - Vidya S. Viswanathan
- Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaGeorgiaUSA
| | - Vlad C. Sandulache
- Baylor College of MedicineHoustonTexasUSA,Otolaryngology‐Head and Neck SurgeryOperative Care Line, Michael E. DeBakey Veterans Affairs Medical CenterHoustonTexasUSA
| | - Pingfu Fu
- Department of Population and Quantitative Health SciencesCase Western Reserve UniversityClevelandOhioUSA
| | | | - Quintin Pan
- Case Comprehensive Cancer CenterCase Western Reserve UniversityClevelandOhioUSA
| | - Zelin Zhang
- Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaGeorgiaUSA
| | - Jun Xu
- Nanjing University of Information Science and TechnologyNanjingChina
| | | | | | - Farhoud Faraji
- University of California San DiegoSan DiegoCaliforniaUSA
| | | | - Mitra Mehrad
- Vanderbilt University Medical CenterNashvilleTennesseeUSA
| | | | | | | | | | | | - Jay K. Wasman
- School of MedicineCase Western Reserve UniversityClevelandOhioUSA
| | | | | | | | | | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaGeorgiaUSA,Atlanta Veterans Administration Medical CenterAtlantaGeorgiaUSA,Louis Stokes Cleveland Veterans Affairs Medical CenterClevelandOhioUSA
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4
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Wu Y, Koyuncu CF, Toro P, Corredor G, Feng Q, Buzzy C, Old M, Teknos T, Connelly ST, Jordan RC, Lang Kuhs KA, Lu C, Lewis JS, Madabhushi A. A machine learning model for separating epithelial and stromal regions in oral cavity squamous cell carcinomas using H&E-stained histology images: A multi-center, retrospective study. Oral Oncol 2022; 131:105942. [PMID: 35689952 DOI: 10.1016/j.oraloncology.2022.105942] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/12/2022] [Accepted: 05/24/2022] [Indexed: 01/30/2023]
Abstract
OBJECTIVE Tissue slides from Oral cavity squamous cell carcinoma (OC-SCC), particularly the epithelial regions, hold morphologic features that are both diagnostic and prognostic. Yet, previously developed approaches for automated epithelium segmentation in OC-SCC have not been independently tested in a multi-center setting. In this study, we aimed to investigate the effectiveness and applicability of a convolutional neural network (CNN) model to perform epithelial segmentation using digitized H&E-stained diagnostic slides from OC-SCC patients in a multi-center setting. METHODS A CNN model was developed to segment the epithelial regions of digitized slides (n = 810), retrospectively collected from five different centers. Deep learning models were trained and validated using well-annotated tissue microarray (TMA) images (n = 212) at various magnifications. The best performing model was locked down and used for independent testing with a total of 478 whole-slide images (WSIs). Manually annotated epithelial regions were used as the reference standard for evaluation. We also compared the model generated results with IHC-stained epithelium (n = 120) as the reference. RESULTS The locked-down CNN model trained on the TMA image training cohorts with 10x magnification achieved the best segmentation performance. The locked-down model performed consistently and yielded Pixel Accuracy, Recall Rate, Precision Rate, and Dice Coefficient that ranged from 95.8% to 96.6%, 79.1% to 93.8%, 85.7% to 89.3%, and 82.3% to 89.0%, respectively for the three independent testing WSI cohorts. CONCLUSION The automated model achieved a consistently accurate performance for automated epithelial region segmentation compared to manual annotations. This model could be integrated into a computer-aided diagnosis or prognosis system.
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Affiliation(s)
- Yuxin Wu
- Shandong Junteng Medical Technology Co., Ltd, Jinan, China; College of Computer Science, Shaanxi Normal University, Xian, China
| | - Can F Koyuncu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Paula Toro
- Department of Pathology, Cleveland Clinic, OH, USA
| | - German Corredor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Qianyu Feng
- College of Computer Science, Shaanxi Normal University, Xian, China
| | - Christina Buzzy
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Matthew Old
- Department of Otolaryngology, Ohio State University Medical Center, OH, USA
| | - Theodoros Teknos
- Department of Otolaryngology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Stephen Thaddeus Connelly
- Department of Oral and Maxillofacial Surgery, San Francisco Veterans Affairs Health Care System, University of California, San Francisco, San Francisco, CA, USA
| | - Richard C Jordan
- Departments of Orofacial Sciences, Pathology and Radiation Oncology, University of California San Francisco, CA, USA
| | - Krystle A Lang Kuhs
- Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, USA; Department of Medicine, Vanderbilt University Medical Cancer, Nashville, TN, USA
| | - Cheng Lu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - James S Lewis
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Otolaryngology - Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA.
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5
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Koyuncu CF, Lu C, Bera K, Zhang Z, Xu J, Toro P, Corredor G, Chute D, Fu P, Thorstad WL, Faraji F, Bishop JA, Mehrad M, Castro PD, Sikora AG, Thompson LD, Chernock RD, Lang Kuhs KA, Luo J, Sandulache V, Adelstein DJ, Koyfman S, Lewis JS, Madabhushi A. Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma. J Clin Invest 2021; 131:145488. [PMID: 33651718 DOI: 10.1172/jci145488] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 02/25/2021] [Indexed: 12/12/2022] Open
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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Affiliation(s)
- Can F Koyuncu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.,Louis Stokes Cleveland Veterans Affairs (VA) Medical Center, Cleveland, Ohio, USA
| | - Cheng Lu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Zelin Zhang
- Nanjing University of Information Science and Technology, Nanjing, China
| | - Jun Xu
- Nanjing University of Information Science and Technology, Nanjing, China
| | - Paula Toro
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - German Corredor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.,Louis Stokes Cleveland Veterans Affairs (VA) Medical Center, Cleveland, Ohio, USA
| | | | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Wade L Thorstad
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Justin A Bishop
- University of Texas (UT) Southwestern Medical Center, Dallas, Texas, USA
| | - Mitra Mehrad
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Patricia D Castro
- Department of Otolaryngology, Head and Neck Surgery, Baylor College of Medicine, Houston, Texas, USA
| | - Andrew G Sikora
- Department of Otolaryngology, Head and Neck Surgery, Baylor College of Medicine, Houston, Texas, USA.,ENT Section, Operative Care Line, Michael E. DeBakey VA Medical Center, Houston, Texas, USA
| | | | - R D Chernock
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Jingqin Luo
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Vlad Sandulache
- Department of Otolaryngology, Head and Neck Surgery, Baylor College of Medicine, Houston, Texas, USA.,ENT Section, Operative Care Line, Michael E. DeBakey VA Medical Center, Houston, Texas, USA
| | | | | | - James S Lewis
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.,Louis Stokes Cleveland Veterans Affairs (VA) Medical Center, Cleveland, Ohio, USA
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