4.6 Article

Automated PD-L1 Scoring Using Artificial Intelligence in Head and Neck Squamous Cell Carcinoma

Related references

Note: Only part of the references are listed.
Article Pathology

Comparison of three PD-L1 immunohistochemical assays in head and neck squamous cell carcinoma (HNSCC)

Emma J. de Ruiter et al.

Summary: This study compared the performance of two PD-L1 standardized assays and one laboratory-developed test in head and neck squamous cell carcinoma, revealing moderate concordance between different staining assays and considerable differences in PD-L1 positivity when using clinically relevant cutoffs. These findings suggest caution is needed when using PD-L1 expression to guide clinical practice.

MODERN PATHOLOGY (2021)

Article Computer Science, Artificial Intelligence

Deep neural network models for computational histopathology: A survey

Chetan L. Srinidhi et al.

Summary: This paper presents a comprehensive review of state-of-the-art deep learning approaches used in histopathological image analysis. Through a survey of over 130 papers, the progress in the field based on different machine learning strategies is reviewed. Additionally, the paper discusses the application of deep learning in survival models and highlights the challenges and limitations of current deep learning methods, as well as potential directions for future research.

MEDICAL IMAGE ANALYSIS (2021)

Article Oncology

PD-L1 Testing and Squamous Cell Carcinoma of the Head and Neck: A Multicenter Study on the Diagnostic Reproducibility of Different Protocols

Simona Crosta et al.

Summary: This study aimed to evaluate the performance of different PD-L1 staining protocols in head and neck squamous cell carcinoma, showing moderate interobserver reliability among the different protocols.

CANCERS (2021)

Article Computer Science, Interdisciplinary Applications

Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC) Scoring and Survival Analysis on PD-L1 Stained Tissue Images

Ansh Kapil et al.

Summary: Two deep learning-based decision systems have been developed to stratify NSCLC patients treated with checkpoint inhibitor therapy into two distinct survival groups, with one system replicating pathologist TC scoring and the other learning patient stratification directly from overall survival time and event information.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

Article Oncology

Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software

Julia R. Naso et al.

Summary: PD-L1 expression in non-small cell lung cancer is predictive of response to immunotherapy, and automated PD-L1 scoring using QuPath software shows excellent correlation with manual scoring by pathologists. However, automated scoring tends to result in more 1-49% scores compared to manual scoring. Additionally, automated scoring shows high sensitivity but lower specificity at a 1% threshold, and excellent specificity but lower sensitivity at a 50% threshold.

PATHOLOGY & ONCOLOGY RESEARCH (2021)

Article Oncology

Pan-cancer image-based detection of clinically actionable genetic alterations

Jakob Nikolas Kather et al.

NATURE CANCER (2020)

Article Oncology

Next generation sequencing of PD-L1 for predicting response to immune checkpoint inhibitors

Jeffrey M. Conroy et al.

JOURNAL FOR IMMUNOTHERAPY OF CANCER (2019)

Review Oncology

The changing therapeutic landscape of head and neck cancer

John D. Cramer et al.

NATURE REVIEWS CLINICAL ONCOLOGY (2019)

Review Oncology

Optimizing treatments for recurrent or metastatic head and neck squamous cell carcinoma

Pol Specenier et al.

EXPERT REVIEW OF ANTICANCER THERAPY (2018)

Article Cell Biology

Digital image analysis improves precision of PD-L1 scoring in cutaneous melanoma

Viktor H. Koelzer et al.

HISTOPATHOLOGY (2018)

Article Computer Science, Interdisciplinary Applications

NeuralNetTools: Visualization and Analysis Tools for Neural Networks

Marcus W. Beck

JOURNAL OF STATISTICAL SOFTWARE (2018)

Article Multidisciplinary Sciences

QuPath: Open source software for digital pathology image analysis

Peter Bankhead et al.

SCIENTIFIC REPORTS (2017)

Review Medical Laboratory Technology

The Gold Standard Paradox in Digital Image Analysis Manual Versus Automated Scoring as Ground Truth

Famke Aeffner et al.

ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE (2017)

Article Rehabilitation

A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research

Terry K. Koo et al.

JOURNAL OF CHIROPRACTIC MEDICINE (2016)

Review Reproductive Biology

Method agreement analysis: A review of correct methodology

P. F. Watson et al.

THERIOGENOLOGY (2010)

Article Otorhinolaryngology

NATIONAL CANCER DATABASE REPORT ON CANCER OF THE HEAD AND NECK: 10-YEAR UPDATE

Jay S. Cooper et al.

HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK (2009)